library(dplyr)
##
## Attaching package: 'dplyr'
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
library(stringr)
library(tm)
## Loading required package: NLP
library(wordcloud)
## Loading required package: RColorBrewer
library(plyr)
## -------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## -------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
library(arules)
## Loading required package: Matrix
##
## Attaching package: 'arules'
## The following object is masked from 'package:tm':
##
## inspect
## The following object is masked from 'package:dplyr':
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## recode
## The following objects are masked from 'package:base':
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## abbreviate, write
library(arulesViz)
## Loading required package: grid
library(igraph)
##
## Attaching package: 'igraph'
## The following object is masked from 'package:arules':
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## union
## The following objects are masked from 'package:dplyr':
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## as_data_frame, groups, union
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## union
Q1)
papers <- read.csv("4th Assignment Text Mining 2013170833.csv")
p_num <- nrow(papers)
p_num
## [1] 2027
Q2)
years <- NULL
for(i in 1:p_num){
tmp_years <- lapply(strsplit(as.String(papers$meta[i]), ' ', fixed = T),'[',4) %>% unlist
years <- c(years, tmp_years)
}
years_tab <- table(years)
barplot(years_tab ,main='Submission Years',xlab='Years group',ylab='Proportion')
pie(years_tab,main='Submission Years')
mosaicplot(years_tab ,main='Submission Years',xlab='Years group')
Q3)
num_auth <- 0
for(i in 1:p_num){
tmp_num <- length(strsplit(as.String(papers$author[i]), ',', fixed = T) %>% unlist)
if(num_auth<tmp_num){
num_row <- i
num_auth <- tmp_num
}
}
papers$title[num_row]
## [1] Deep Learning for Quality Control of Subcortical Brain 3D Shape Models
## 2026 Levels: #phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter ...
strsplit(as.String(papers$author[num_row]), ',', fixed = T)
## [[1]]
## [1] "Dmitry Petrov" " Boris A. Gutman Egor Kuznetsov"
## [3] " Theo G.M. van Erp" " Jessica A. Turner"
## [5] " Lianne Schmaal" " Dick Veltman"
## [7] " Lei Wang" " Kathryn Alpert"
## [9] " Dmitry Isaev" " Artemis Zavaliangos-Petropulu"
## [11] " Christopher R.K. Ching" " Vince Calhoun"
## [13] " David Glahn" " Theodore D. Satterthwaite"
## [15] " Ole Andreas Andreassen" " Stefan Borgwardt"
## [17] " Fleur Howells" " Nynke Groenewold"
## [19] " Aristotle Voineskos" " Joaquim Radua"
## [21] " Steven G. Potkin" " Benedicto Crespo-Facorro"
## [23] " Diana Tordesillas-Gutierrez" " Li Shen"
## [25] " Irina Lebedeva" " Gianfranco Spalletta"
## [27] " Gary Donohoe" " Peter Kochunov"
## [29] " Pedro G.P. Rosa" " Anthony James"
## [31] " Udo Dannlowski" " Bernhard T. Baune"
## [33] " Andre Aleman" " Ian H. Gotlib"
## [35] " Henrik Walter" " Martin Walter"
## [37] " Jair C. Soares" " Stefan Ehrlich"
## [39] " Ruben C. Gur" " N. Trung Doan"
## [41] " Ingrid Agartz" " Lars T. Westlye"
## [43] " Fabienne Harrisberger" " Anita Riecher-Rossler"
## [45] " Anne Uhlmann" " Dan J. Stein"
## [47] " Erin W. Dickie" " Edith Pomarol-Clotet"
## [49] " Paola Fuentes-Claramonte" " Erick Jorge Canales-Rodriguez"
## [51] " Raymond Salvador" " Alexander J. Huang"
## [53] " Roberto Roiz-Santianez" " Shan Cong"
## [55] " Alexander Tomyshev" " Fabrizio Piras"
## [57] " Daniela Vecchio" " Nerisa Banaj"
## [59] " Valentina Ciullo" " Elliot Hong"
## [61] " Geraldo Busatto" " Marcus V. Zanetti"
## [63] " Mauricio H. Serpa" " Simon Cervenka"
## [65] " Sinead Kelly" " Dominik Grotegerd"
## [67] " Matthew D. Sacchet" " Ilya M. Veer"
## [69] " Meng Li" " Mon-Ju Wu"
## [71] " Benson Irungu" " Esther Walton"
## [73] " Paul M. Thompson"
Q4)
len_title <- 0
for(i in 1:p_num){
tmp_num <- nchar(as.String(papers$title[i]))
if(len_title<tmp_num){
num_row <- i
len_title <- tmp_num
}
}
papers$title[num_row]
## [1] Phase 4: DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real-Time Recognition and Localization of Marine Mammals - Distributed Processing and Big Data Applications
## 2026 Levels: #phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter ...
Q5)
years_list <- strsplit(levels(factor(years)), ' ', fixed = T)
len_title <- NULL
len_abs <- NULL
num_auth <- NULL
for(i in years_list){
count <- 0
tmp_len_title <- 0
tmp_len_abs <- 0
tmp_num_auth <- 0
for(j in 1:p_num){
if(years[j] == i){
count <- count + 1
tmp_len_title <- tmp_len_title + nchar(as.String(papers$title[j]))
tmp_len_abs <- tmp_len_abs + nchar(as.String(papers$abstract[j]))
tmp_num_auth <- tmp_num_auth + length(strsplit(as.String(papers$author[j]), ',', fixed = T) %>% unlist)
}
}
len_title <- c(len_title, tmp_len_title/count)
len_abs <- c(len_abs, tmp_len_abs/count)
num_auth <- c(num_auth, tmp_num_auth/count)
}
data.frame(len_title, len_abs, num_auth, row.names = years_list)
## len_title len_abs num_auth
## 2011 41.00000 1296.0000 2.000000
## 2013 55.73333 800.6667 2.400000
## 2014 63.20000 1035.9333 3.233333
## 2015 71.08824 1100.3922 3.921569
## 2016 72.35865 1136.5485 4.050633
## 2017 75.74920 1125.7428 4.070740
## 2018 77.00000 1161.1892 4.714706
idx_list <- list()
for(i in years_list){
tmp <- which(years==i)
idx_list[length(idx_list)+1] <- list(tmp)
}
years_list[length(years_list)+1] <- list("all")
idx_list[length(idx_list)+1] <- list(1:length(years))
names(idx_list) <- years_list
abs_list <- NULL
for(i in 1:length(years_list)){
abs_tmp <- as.data.frame(papers$abstract[idx_list[i] %>% unlist])
abs_list <- c(abs_list, abs_tmp)
}
# Construct a list of corpuses
corp_list <- list()
for(i in 1:length(years_list)){
corp_tmp <- Corpus(VectorSource(as.factor(abs_list[i] %>% unlist)))
corp_list[i] <- list(corp_tmp)
}
# Data preprocessing
# deep learning으로 검색하였으므로, redundancy를 예상하여 "deep"과 "learning"은 제외했다.
myStopwords <- c(stopwords("SMART"), "deep", "learning")
prep_list <- list()
for(i in 1:length(years_list)){
prep_temp <- tm_map(corp_list[[i]], content_transformer(tolower))
prep_temp <- tm_map(prep_temp, content_transformer(removePunctuation))
prep_temp <- tm_map(prep_temp, content_transformer(removeNumbers))
prep_temp <- tm_map(prep_temp, removeWords, myStopwords)
prep_temp <- tm_map(prep_temp, stemDocument)
prep_list[i] <- list(prep_temp)
}
## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents
## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents
## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents
## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents
## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents
## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents
## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents
## Warning in tm_map.SimpleCorpus(corp_list[[i]],
## content_transformer(tolower)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removePunctuation)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp,
## content_transformer(removeNumbers)): transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, removeWords, myStopwords):
## transformation drops documents
## Warning in tm_map.SimpleCorpus(prep_temp, stemDocument): transformation
## drops documents
# Construct Term-Document Matrix, Word cloud
tdm_list <- list()
wc_list <- list()
for(i in 1:length(years_list)){
tmp_tdm <- TermDocumentMatrix(prep_list[[i]], control = list(minWordLength = 1))
tdm_list[i] <- list(tmp_tdm)
tmp_wc <- as.matrix(tmp_tdm)
wc_list[i] <- list(tmp_wc)
}
Q6)
sparsity <- c(0, 89, 93, 97, 98, 99, 99)
dat <- data.frame(years_list[1:7] %>% unlist, sparsity, stringsAsFactors=FALSE)
dat
## years_list.1.7......unlist sparsity
## 1 2011 0
## 2 2013 89
## 3 2014 93
## 4 2015 97
## 5 2016 98
## 6 2017 99
## 7 2018 99
Q7)
word_freq <- sort(rowSums(wc_list[[length(wc_list)]]), decreasing=TRUE)
word_freq[1:50]
## network model imag data train method
## 3025 2553 2051 2033 1819 1757
## neural propos perform approach result system
## 1740 1511 1408 1212 1171 1068
## predict featur dataset algorithm base paper
## 997 981 970 938 933 922
## show comput convolut detect problem task
## 888 871 823 810 743 735
## classif accuraci architectur applic framework improv
## 718 690 682 676 637 632
## achiev work present machin demonstr process
## 622 618 614 602 601 585
## high inform set techniqu challeng time
## 570 564 558 557 556 542
## develop compar studi recent learn segment
## 538 525 512 504 495 481
## provid optim
## 475 474
Q8)
wf_list <- list()
for(i in 1:(length(years_list))){
wd_tmp <- sort(rowSums(wc_list[[i]]), decreasing=TRUE)
wf_list[i] <- list(wd_tmp)
}
for(i in 1:(length(years_list)-1)){
barplot(wf_list[[i]][1:8], main='Frequent Words',xlab='Words',ylab='Frequency')
}
network 어는 항상 가장 많이 쓰이는 단어였다.(2011년은 표본이 1개라 제외) 과거에 비해 ‘model’, ‘data’, ’image’의 사용 빈도가 늘어나고 있으며, ’represent’는 사용빈도가 줄어들고 있다.
Q9)
for(i in 1:length(years_list)){
name_tmp <- names(wf_list[[i]])
wcd_tmp <- data.frame(word=name_tmp, freq=wf_list[[i]])
pal <- brewer.pal(8, "Dark2")
wordcloud(wcd_tmp$word, wcd_tmp$freq, min.freq=300, scale = c(3, 0.1),
rot.per = 0.1, col=pal, random.order=F)
}
image라는 단어가 최근에 많이 등장했는데, 최근 산업에서 deep learning이 시각 인식에 많이 접목됨을 추측할 있다.
Q10)
for(i in 4:length(years_list)){
tmp_wcm <- wc_list[[i]]
tmp_wcm[tmp_wcm >= 1] <- 1
freq_idx1 <- which(rowSums(tmp_wcm) > length(idx_list[[i]])/3)
freq_wcmat1 <- wc_list[[i]][freq_idx1,]
# Transform into a term-term adjacency matrix
termMatrix1 <- freq_wcmat1 %*% t(freq_wcmat1)
# inspect terms numbered 5 to 10
termMatrix1[1:10,1:10]
g1 <- graph.adjacency(termMatrix1, weighted=T, mode = "undirected")
g1 <- simplify(g1)
V(g1)$label <- V(g1)$name
V(g1)$degree <- degree(g1)
g1 <- delete.edges(g1, which(E(g1)$weight <= 3))
set.seed(3952)
layout1 <- layout.fruchterman.reingold(g1)
# Make the network look better
V(g1)$label.cex <- V(g1)$degree/max(V(g1)$degree)
V(g1)$label.color <- rgb(0, 0, 0.2, 0.8)
V(g1)$frame.color <- NA
egam1 <- 3*(log(E(g1)$weight+1))/max(log(E(g1)$weight+1))
E(g1)$color <- rgb(0.5, 0.5, 0)
E(g1)$width <- egam1
# plot the graph in layout1
plot(g1, layout=layout.kamada.kawai)
}
’perform’과 ’train’의 밀접한 관계를 볼 수 있다. train에 따라 신경망 model의 성능이 결정되기 때문이다. (2013년도까지는 표본이 적어서 임의로 제외하였음.)
Q11)
insp_list <- NULL
for(i in 3:length(years_list)){
tmp_tran <- as.matrix(t(tdm_list[[i]]))
tmp_tran <- as(tmp_tran, "transactions")
tmp_rules <- apriori(tmp_tran, parameter=list(minlen=2,supp=0.1, conf=0.7))
insp_list[i] <- list(inspect(tmp_rules)[1:10,])
}
## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 3
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[846 item(s), 30 transaction(s)] done [0.00s].
## sorting and recoding items ... [170 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 done [0.00s].
## writing ... [28995 rule(s)] done [0.01s].
## creating S4 object ... done [0.04s].
## lhs rhs support confidence lift count
## [1] {prior} => {represent} 0.1000000 1.0000000 2.000000 3
## [2] {prior} => {model} 0.1000000 1.0000000 1.875000 3
## [3] {shown} => {problem} 0.1000000 1.0000000 3.333333 3
## [4] {shown} => {method} 0.1000000 1.0000000 2.727273 3
## [5] {shown} => {show} 0.1000000 1.0000000 1.875000 3
## [6] {convent} => {problem} 0.1000000 1.0000000 3.333333 3
## [7] {convent} => {method} 0.1000000 1.0000000 2.727273 3
## [8] {convent} => {show} 0.1000000 1.0000000 1.875000 3
## [9] {current} => {perform} 0.1000000 1.0000000 2.142857 3
## [10] {current} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11] {current} => {featur} 0.1000000 1.0000000 1.875000 3
## [12] {infer} => {make} 0.1000000 1.0000000 3.333333 3
## [13] {infer} => {paper} 0.1000000 1.0000000 3.000000 3
## [14] {infer} => {data} 0.1000000 1.0000000 2.307692 3
## [15] {infer} => {model} 0.1000000 1.0000000 1.875000 3
## [16] {artifici} => {machin} 0.1000000 1.0000000 4.285714 3
## [17] {human} => {propos} 0.1000000 1.0000000 2.000000 3
## [18] {human} => {featur} 0.1000000 1.0000000 1.875000 3
## [19] {bayesian} => {applic} 0.1000000 1.0000000 4.285714 3
## [20] {bayesian} => {perform} 0.1000000 1.0000000 2.142857 3
## [21] {obtain} => {propos} 0.1000000 1.0000000 2.000000 3
## [22] {address} => {problem} 0.1000000 1.0000000 3.333333 3
## [23] {address} => {perform} 0.1000000 1.0000000 2.142857 3
## [24] {memori} => {reduc} 0.1000000 1.0000000 4.285714 3
## [25] {memori} => {network} 0.1000000 1.0000000 1.578947 3
## [26] {main} => {make} 0.1000000 1.0000000 3.333333 3
## [27] {search} => {recent} 0.1000000 1.0000000 4.285714 3
## [28] {code} => {task} 0.1000000 1.0000000 2.727273 3
## [29] {larger} => {result} 0.1000000 1.0000000 3.000000 3
## [30] {larger} => {approach} 0.1000000 1.0000000 2.500000 3
## [31] {larger} => {dataset} 0.1000000 1.0000000 2.307692 3
## [32] {larger} => {propos} 0.1000000 1.0000000 2.000000 3
## [33] {larger} => {model} 0.1000000 1.0000000 1.875000 3
## [34] {label} => {task} 0.1000000 1.0000000 2.727273 3
## [35] {label} => {data} 0.1000000 1.0000000 2.307692 3
## [36] {label} => {featur} 0.1000000 1.0000000 1.875000 3
## [37] {ident} => {face} 0.1000000 1.0000000 7.500000 3
## [38] {face} => {ident} 0.1000000 0.7500000 7.500000 3
## [39] {ident} => {challeng} 0.1000000 1.0000000 6.000000 3
## [40] {ident} => {recognit} 0.1000000 1.0000000 3.333333 3
## [41] {ident} => {train} 0.1000000 1.0000000 2.500000 3
## [42] {ident} => {represent} 0.1000000 1.0000000 2.000000 3
## [43] {standard} => {make} 0.1000000 1.0000000 3.333333 3
## [44] {standard} => {problem} 0.1000000 1.0000000 3.333333 3
## [45] {standard} => {perform} 0.1000000 1.0000000 2.142857 3
## [46] {standard} => {model} 0.1000000 1.0000000 1.875000 3
## [47] {pose} => {dataset} 0.1000000 1.0000000 2.307692 3
## [48] {pose} => {propos} 0.1000000 1.0000000 2.000000 3
## [49] {pose} => {featur} 0.1000000 1.0000000 1.875000 3
## [50] {tradit} => {shallow} 0.1000000 1.0000000 6.000000 3
## [51] {tradit} => {improv} 0.1000000 1.0000000 3.333333 3
## [52] {tradit} => {perform} 0.1000000 1.0000000 2.142857 3
## [53] {tradit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [54] {tradit} => {featur} 0.1000000 1.0000000 1.875000 3
## [55] {factor} => {model} 0.1000000 0.7500000 1.406250 3
## [56] {spars} => {perform} 0.1000000 1.0000000 2.142857 3
## [57] {spars} => {represent} 0.1000000 1.0000000 2.000000 3
## [58] {spars} => {propos} 0.1000000 1.0000000 2.000000 3
## [59] {oper} => {task} 0.1000000 1.0000000 2.727273 3
## [60] {oper} => {data} 0.1000000 1.0000000 2.307692 3
## [61] {discrimin} => {perform} 0.1000000 1.0000000 2.142857 3
## [62] {discrimin} => {learn} 0.1000000 1.0000000 2.307692 3
## [63] {discrimin} => {model} 0.1000000 1.0000000 1.875000 3
## [64] {discrimin} => {featur} 0.1000000 1.0000000 1.875000 3
## [65] {construct} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [66] {construct} => {show} 0.1000000 0.7500000 1.406250 3
## [67] {construct} => {model} 0.1000000 0.7500000 1.406250 3
## [68] {advantag} => {classif} 0.1000000 1.0000000 3.750000 3
## [69] {advantag} => {method} 0.1000000 1.0000000 2.727273 3
## [70] {advantag} => {approach} 0.1000000 1.0000000 2.500000 3
## [71] {advantag} => {featur} 0.1000000 1.0000000 1.875000 3
## [72] {advantag} => {network} 0.1000000 1.0000000 1.578947 3
## [73] {perceptron} => {paper} 0.1000000 1.0000000 3.000000 3
## [74] {perceptron} => {train} 0.1000000 1.0000000 2.500000 3
## [75] {difficult} => {model} 0.1000000 1.0000000 1.875000 3
## [76] {equival} => {complex} 0.1000000 1.0000000 5.000000 3
## [77] {equival} => {featur} 0.1000000 1.0000000 1.875000 3
## [78] {initi} => {layer} 0.1000000 1.0000000 5.000000 3
## [79] {initi} => {work} 0.1000000 1.0000000 2.500000 3
## [80] {initi} => {network} 0.1000000 1.0000000 1.578947 3
## [81] {nonlinear} => {power} 0.1000000 1.0000000 7.500000 3
## [82] {power} => {nonlinear} 0.1000000 0.7500000 7.500000 3
## [83] {nonlinear} => {perform} 0.1000000 1.0000000 2.142857 3
## [84] {nonlinear} => {featur} 0.1000000 1.0000000 1.875000 3
## [85] {finetun} => {framework} 0.1000000 1.0000000 5.000000 3
## [86] {finetun} => {train} 0.1000000 1.0000000 2.500000 3
## [87] {finetun} => {work} 0.1000000 1.0000000 2.500000 3
## [88] {singl} => {accuraci} 0.1000000 0.7500000 3.750000 3
## [89] {singl} => {achiev} 0.1000000 0.7500000 3.214286 3
## [90] {singl} => {network} 0.1000000 0.7500000 1.184211 3
## [91] {local} => {imag} 0.1000000 1.0000000 6.000000 3
## [92] {local} => {perform} 0.1000000 1.0000000 2.142857 3
## [93] {local} => {show} 0.1000000 1.0000000 1.875000 3
## [94] {local} => {propos} 0.1000000 1.0000000 2.000000 3
## [95] {speech} => {layer} 0.1000000 1.0000000 5.000000 3
## [96] {speech} => {result} 0.1000000 1.0000000 3.000000 3
## [97] {previous} => {network} 0.1000000 1.0000000 1.578947 3
## [98] {field} => {machin} 0.1000000 1.0000000 4.285714 3
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## [100] {field} => {learn} 0.1000000 1.0000000 2.307692 3
## [101] {field} => {featur} 0.1000000 1.0000000 1.875000 3
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## [103] {engin} => {represent} 0.1000000 1.0000000 2.000000 3
## [104] {engin} => {featur} 0.1000000 1.0000000 1.875000 3
## [105] {easili} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [106] {easili} => {task} 0.1000000 1.0000000 2.727273 3
## [107] {easili} => {data} 0.1000000 1.0000000 2.307692 3
## [108] {form} => {input} 0.1000000 0.7500000 3.214286 3
## [109] {form} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [110] {form} => {network} 0.1000000 0.7500000 1.184211 3
## [111] {pretrain} => {show} 0.1000000 1.0000000 1.875000 3
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## [113] {under} => {method} 0.1000000 0.7500000 2.045455 3
## [114] {under} => {train} 0.1000000 0.7500000 1.875000 3
## [115] {under} => {data} 0.1000000 0.7500000 1.730769 3
## [116] {under} => {show} 0.1333333 1.0000000 1.875000 4
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## [119] {transfer} => {data} 0.1000000 1.0000000 2.307692 3
## [120] {transfer} => {network} 0.1000000 1.0000000 1.578947 3
## [121] {scheme} => {comput} 0.1000000 1.0000000 4.285714 3
## [122] {great} => {task} 0.1000000 1.0000000 2.727273 3
## [123] {great} => {data} 0.1000000 1.0000000 2.307692 3
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## [128] {common} => {dataset} 0.1000000 1.0000000 2.307692 3
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## [130] {common} => {propos} 0.1000000 1.0000000 2.000000 3
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## [132] {handcraft} => {dataset} 0.1000000 1.0000000 2.307692 3
## [133] {handcraft} => {learn} 0.1000000 1.0000000 2.307692 3
## [134] {handcraft} => {propos} 0.1000000 1.0000000 2.000000 3
## [135] {handcraft} => {model} 0.1000000 1.0000000 1.875000 3
## [136] {handcraft} => {featur} 0.1000000 1.0000000 1.875000 3
## [137] {includ} => {network} 0.1000000 1.0000000 1.578947 3
## [138] {compon} => {experi} 0.1000000 1.0000000 3.750000 3
## [139] {compon} => {dataset} 0.1000000 1.0000000 2.307692 3
## [140] {compon} => {propos} 0.1000000 1.0000000 2.000000 3
## [141] {compon} => {model} 0.1000000 1.0000000 1.875000 3
## [142] {provid} => {neural} 0.1000000 1.0000000 3.000000 3
## [143] {provid} => {train} 0.1000000 1.0000000 2.500000 3
## [144] {provid} => {work} 0.1000000 1.0000000 2.500000 3
## [145] {provid} => {dataset} 0.1000000 1.0000000 2.307692 3
## [146] {provid} => {network} 0.1000000 1.0000000 1.578947 3
## [147] {multipl} => {task} 0.1000000 1.0000000 2.727273 3
## [148] {multipl} => {data} 0.1000000 1.0000000 2.307692 3
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## [150] {test} => {classif} 0.1000000 1.0000000 3.750000 3
## [151] {test} => {propos} 0.1000000 1.0000000 2.000000 3
## [152] {test} => {model} 0.1000000 1.0000000 1.875000 3
## [153] {test} => {featur} 0.1000000 1.0000000 1.875000 3
## [154] {extract} => {general} 0.1000000 0.7500000 3.750000 3
## [155] {extract} => {recognit} 0.1000000 0.7500000 2.500000 3
## [156] {extract} => {result} 0.1333333 1.0000000 3.000000 4
## [157] {extract} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [158] {extract} => {data} 0.1000000 0.7500000 1.730769 3
## [159] {extract} => {show} 0.1333333 1.0000000 1.875000 4
## [160] {extract} => {model} 0.1000000 0.7500000 1.406250 3
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## [162] {report} => {appli} 0.1000000 0.7500000 3.750000 3
## [163] {report} => {result} 0.1000000 0.7500000 2.250000 3
## [164] {report} => {work} 0.1000000 0.7500000 1.875000 3
## [165] {report} => {dataset} 0.1000000 0.7500000 1.730769 3
## [166] {report} => {propos} 0.1000000 0.7500000 1.500000 3
## [167] {report} => {network} 0.1333333 1.0000000 1.578947 4
## [168] {attribut} => {outperform} 0.1000000 1.0000000 7.500000 3
## [169] {outperform} => {attribut} 0.1000000 0.7500000 7.500000 3
## [170] {attribut} => {represent} 0.1000000 1.0000000 2.000000 3
## [171] {attribut} => {show} 0.1000000 1.0000000 1.875000 3
## [172] {employ} => {task} 0.1000000 1.0000000 2.727273 3
## [173] {employ} => {data} 0.1000000 1.0000000 2.307692 3
## [174] {employ} => {model} 0.1000000 1.0000000 1.875000 3
## [175] {employ} => {featur} 0.1000000 1.0000000 1.875000 3
## [176] {abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [177] {abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [178] {abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [179] {abil} => {network} 0.1000000 1.0000000 1.578947 3
## [180] {structur} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [181] {structur} => {perform} 0.1000000 0.7500000 1.607143 3
## [182] {structur} => {data} 0.1000000 0.7500000 1.730769 3
## [183] {structur} => {learn} 0.1000000 0.7500000 1.730769 3
## [184] {structur} => {show} 0.1333333 1.0000000 1.875000 4
## [185] {structur} => {model} 0.1000000 0.7500000 1.406250 3
## [186] {structur} => {featur} 0.1000000 0.7500000 1.406250 3
## [187] {inform} => {learn} 0.1000000 1.0000000 2.307692 3
## [188] {inform} => {show} 0.1000000 1.0000000 1.875000 3
## [189] {inform} => {propos} 0.1000000 1.0000000 2.000000 3
## [190] {inform} => {model} 0.1000000 1.0000000 1.875000 3
## [191] {convolut} => {effici} 0.1000000 0.7500000 4.500000 3
## [192] {convolut} => {reduc} 0.1000000 0.7500000 3.214286 3
## [193] {convolut} => {architectur} 0.1000000 0.7500000 2.812500 3
## [194] {convolut} => {work} 0.1000000 0.7500000 1.875000 3
## [195] {convolut} => {perform} 0.1000000 0.7500000 1.607143 3
## [196] {convolut} => {dataset} 0.1000000 0.7500000 1.730769 3
## [197] {convolut} => {network} 0.1333333 1.0000000 1.578947 4
## [198] {filter} => {propos} 0.1000000 1.0000000 2.000000 3
## [199] {highlevel} => {larg} 0.1000000 1.0000000 6.000000 3
## [200] {highlevel} => {work} 0.1000000 1.0000000 2.500000 3
## [201] {highlevel} => {represent} 0.1000000 1.0000000 2.000000 3
## [202] {care} => {design} 0.1000000 1.0000000 7.500000 3
## [203] {design} => {care} 0.1000000 0.7500000 7.500000 3
## [204] {care} => {task} 0.1000000 1.0000000 2.727273 3
## [205] {care} => {data} 0.1000000 1.0000000 2.307692 3
## [206] {care} => {dataset} 0.1000000 1.0000000 2.307692 3
## [207] {care} => {learn} 0.1000000 1.0000000 2.307692 3
## [208] {care} => {featur} 0.1000000 1.0000000 1.875000 3
## [209] {care} => {network} 0.1000000 1.0000000 1.578947 3
## [210] {rate} => {success} 0.1000000 0.7500000 2.812500 3
## [211] {rate} => {dataset} 0.1000000 0.7500000 1.730769 3
## [212] {rate} => {learn} 0.1000000 0.7500000 1.730769 3
## [213] {rate} => {represent} 0.1000000 0.7500000 1.500000 3
## [214] {rate} => {show} 0.1000000 0.7500000 1.406250 3
## [215] {rate} => {network} 0.1000000 0.7500000 1.184211 3
## [216] {captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [217] {captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [218] {captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [219] {captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [220] {captur} => {network} 0.1000000 1.0000000 1.578947 3
## [221] {paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [222] {paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [223] {paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [224] {paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [225] {time} => {reduc} 0.1000000 0.7500000 3.214286 3
## [226] {time} => {show} 0.1000000 0.7500000 1.406250 3
## [227] {time} => {model} 0.1333333 1.0000000 1.875000 4
## [228] {time} => {network} 0.1000000 0.7500000 1.184211 3
## [229] {encod} => {work} 0.1000000 0.7500000 1.875000 3
## [230] {simpl} => {studi} 0.1000000 1.0000000 7.500000 3
## [231] {studi} => {simpl} 0.1000000 0.7500000 7.500000 3
## [232] {simpl} => {process} 0.1000000 1.0000000 5.000000 3
## [233] {simpl} => {work} 0.1000000 1.0000000 2.500000 3
## [234] {simpl} => {network} 0.1000000 1.0000000 1.578947 3
## [235] {baselin} => {classif} 0.1000000 1.0000000 3.750000 3
## [236] {baselin} => {propos} 0.1000000 1.0000000 2.000000 3
## [237] {increas} => {result} 0.1000000 0.7500000 2.250000 3
## [238] {increas} => {neural} 0.1000000 0.7500000 2.250000 3
## [239] {increas} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [240] {increas} => {train} 0.1000000 0.7500000 1.875000 3
## [241] {increas} => {dataset} 0.1000000 0.7500000 1.730769 3
## [242] {increas} => {represent} 0.1000000 0.7500000 1.500000 3
## [243] {increas} => {featur} 0.1000000 0.7500000 1.406250 3
## [244] {increas} => {network} 0.1333333 1.0000000 1.578947 4
## [245] {boltzmann} => {restrict} 0.1333333 1.0000000 7.500000 4
## [246] {restrict} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [247] {boltzmann} => {recent} 0.1000000 0.7500000 3.214286 3
## [248] {boltzmann} => {machin} 0.1333333 1.0000000 4.285714 4
## [249] {boltzmann} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [250] {boltzmann} => {task} 0.1000000 0.7500000 2.045455 3
## [251] {boltzmann} => {train} 0.1000000 0.7500000 1.875000 3
## [252] {boltzmann} => {data} 0.1000000 0.7500000 1.730769 3
## [253] {boltzmann} => {show} 0.1333333 1.0000000 1.875000 4
## [254] {boltzmann} => {model} 0.1333333 1.0000000 1.875000 4
## [255] {boltzmann} => {featur} 0.1000000 0.7500000 1.406250 3
## [256] {creat} => {model} 0.1000000 0.7500000 1.406250 3
## [257] {creat} => {network} 0.1333333 1.0000000 1.578947 4
## [258] {restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [259] {restrict} => {machin} 0.1333333 1.0000000 4.285714 4
## [260] {restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [261] {restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [262] {restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [263] {restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [264] {restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [265] {restrict} => {model} 0.1333333 1.0000000 1.875000 4
## [266] {restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [267] {extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [268] {extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [269] {extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [270] {extens} => {model} 0.1000000 1.0000000 1.875000 3
## [271] {present} => {network} 0.1333333 0.8000000 1.263158 4
## [272] {linear} => {framework} 0.1000000 0.7500000 3.750000 3
## [273] {linear} => {perform} 0.1000000 0.7500000 1.607143 3
## [274] {order} => {neural} 0.1000000 0.7500000 2.250000 3
## [275] {order} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [276] {order} => {approach} 0.1000000 0.7500000 1.875000 3
## [277] {order} => {show} 0.1000000 0.7500000 1.406250 3
## [278] {order} => {propos} 0.1000000 0.7500000 1.500000 3
## [279] {order} => {model} 0.1333333 1.0000000 1.875000 4
## [280] {order} => {featur} 0.1000000 0.7500000 1.406250 3
## [281] {order} => {network} 0.1000000 0.7500000 1.184211 3
## [282] {power} => {specif} 0.1000000 0.7500000 4.500000 3
## [283] {power} => {improv} 0.1000000 0.7500000 2.500000 3
## [284] {power} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [285] {power} => {train} 0.1000000 0.7500000 1.875000 3
## [286] {power} => {perform} 0.1000000 0.7500000 1.607143 3
## [287] {power} => {dataset} 0.1000000 0.7500000 1.730769 3
## [288] {power} => {model} 0.1000000 0.7500000 1.406250 3
## [289] {power} => {featur} 0.1000000 0.7500000 1.406250 3
## [290] {benchmark} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [291] {benchmark} => {classif} 0.1000000 0.7500000 2.812500 3
## [292] {benchmark} => {problem} 0.1000000 0.7500000 2.500000 3
## [293] {benchmark} => {perform} 0.1000000 0.7500000 1.607143 3
## [294] {benchmark} => {dataset} 0.1000000 0.7500000 1.730769 3
## [295] {benchmark} => {learn} 0.1333333 1.0000000 2.307692 4
## [296] {benchmark} => {propos} 0.1000000 0.7500000 1.500000 3
## [297] {benchmark} => {model} 0.1000000 0.7500000 1.406250 3
## [298] {benchmark} => {featur} 0.1333333 1.0000000 1.875000 4
## [299] {high} => {success} 0.1000000 0.7500000 2.812500 3
## [300] {high} => {object} 0.1000000 0.7500000 2.812500 3
## [301] {high} => {classif} 0.1000000 0.7500000 2.812500 3
## [302] {high} => {task} 0.1000000 0.7500000 2.045455 3
## [303] {high} => {learn} 0.1000000 0.7500000 1.730769 3
## [304] {high} => {represent} 0.1000000 0.7500000 1.500000 3
## [305] {high} => {propos} 0.1333333 1.0000000 2.000000 4
## [306] {high} => {featur} 0.1333333 1.0000000 1.875000 4
## [307] {parallel} => {framework} 0.1000000 0.7500000 3.750000 3
## [308] {parallel} => {comput} 0.1000000 0.7500000 3.214286 3
## [309] {parallel} => {reduc} 0.1000000 0.7500000 3.214286 3
## [310] {parallel} => {work} 0.1000000 0.7500000 1.875000 3
## [311] {parallel} => {network} 0.1333333 1.0000000 1.578947 4
## [312] {predict} => {outperform} 0.1000000 0.7500000 5.625000 3
## [313] {outperform} => {predict} 0.1000000 0.7500000 5.625000 3
## [314] {predict} => {challeng} 0.1000000 0.7500000 4.500000 3
## [315] {predict} => {paper} 0.1000000 0.7500000 2.250000 3
## [316] {predict} => {train} 0.1333333 1.0000000 2.500000 4
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## [318] {predict} => {data} 0.1000000 0.7500000 1.730769 3
## [319] {predict} => {represent} 0.1000000 0.7500000 1.500000 3
## [320] {predict} => {show} 0.1000000 0.7500000 1.406250 3
## [321] {predict} => {propos} 0.1000000 0.7500000 1.500000 3
## [322] {predict} => {model} 0.1000000 0.7500000 1.406250 3
## [323] {predict} => {featur} 0.1000000 0.7500000 1.406250 3
## [324] {number} => {model} 0.1333333 0.8000000 1.500000 4
## [325] {face} => {variat} 0.1000000 0.7500000 5.625000 3
## [326] {variat} => {face} 0.1000000 0.7500000 5.625000 3
## [327] {face} => {challeng} 0.1000000 0.7500000 4.500000 3
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## [329] {face} => {recognit} 0.1333333 1.0000000 3.333333 4
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## [331] {face} => {data} 0.1000000 0.7500000 1.730769 3
## [332] {face} => {dataset} 0.1000000 0.7500000 1.730769 3
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## [338] {sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [339] {sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [340] {sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [341] {sourc} => {represent} 0.1000000 0.7500000 1.500000 3
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## [343] {sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [344] {sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [345] {variat} => {general} 0.1000000 0.7500000 3.750000 3
## [346] {variat} => {recognit} 0.1333333 1.0000000 3.333333 4
## [347] {variat} => {task} 0.1000000 0.7500000 2.045455 3
## [348] {variat} => {data} 0.1000000 0.7500000 1.730769 3
## [349] {variat} => {learn} 0.1000000 0.7500000 1.730769 3
## [350] {variat} => {represent} 0.1000000 0.7500000 1.500000 3
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## [352] {variat} => {featur} 0.1000000 0.7500000 1.406250 3
## [353] {introduc} => {process} 0.1000000 0.7500000 3.750000 3
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## [359] {introduc} => {network} 0.1000000 0.7500000 1.184211 3
## [360] {import} => {task} 0.1333333 1.0000000 2.727273 4
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## [383] {state} => {dataset} 0.1000000 0.7500000 1.730769 3
## [384] {state} => {learn} 0.1333333 1.0000000 2.307692 4
## [385] {state} => {propos} 0.1333333 1.0000000 2.000000 4
## [386] {state} => {model} 0.1333333 1.0000000 1.875000 4
## [387] {state} => {featur} 0.1000000 0.7500000 1.406250 3
## [388] {joint} => {general} 0.1000000 0.7500000 3.750000 3
## [389] {joint} => {object} 0.1000000 0.7500000 2.812500 3
## [390] {joint} => {method} 0.1000000 0.7500000 2.045455 3
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## [978] {extract,
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## [985] {recognit,
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## [1003] {algorithm,
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## [1006] {show,
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## [1009] {show,
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## [1010] {featur,
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## [1012] {dataset,
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## [1013] {appli,
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## [1014] {appli,
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## [1015] {propos,
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## [1016] {appli,
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## [1017] {network,
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## [1018] {network,
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## [1019] {report,
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## [1020] {network,
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## [1021] {report,
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## [1022] {network,
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## [1027] {propos,
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## [1028] {network,
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## [1031] {outperform,
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## [1032] {attribut,
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## [1033] {attribut,
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## [1050] {approach,
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## [1051] {approach,
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## [1054] {achiev,
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## [1055] {network,
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## [1056] {approach,
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## [1059] {network,
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## [1060] {propos,
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## [1061] {network,
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## [1071] {show,
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## [1072] {data,
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## [1075] {show,
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## [1095] {architectur,
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## [1096] {architectur,
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## [1097] {convolut,
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## [1098] {convolut,
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## [1099] {effici,
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## [1100] {convolut,
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## [1102] {perform,
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## [1103] {convolut,
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## [1104] {network,
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## [1105] {network,
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## [1106] {reduc,
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## [1107] {network,
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## [1108] {architectur,
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## [1109] {convolut,
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## [1111] {perform,
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## [1112] {architectur,
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## [1113] {network,
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## [1114] {convolut,
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## [1115] {perform,
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## [1116] {convolut,
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## [1117] {network,
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## [1119] {network,
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## [1120] {dataset,
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## [1121] {network,
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## [1123] {work,
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## [1164] {network,
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## [1169] {dataset,
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## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [1192] {represent,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [1193] {approach,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [1194] {featur,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [1195] {approach,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [1196] {network,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [1197] {captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [1198] {represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [1199] {captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [1200] {featur,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [1201] {captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [1202] {network,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [1203] {represent,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [1204] {featur,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [1205] {represent,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [1206] {network,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [1207] {featur,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [1208] {network,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [1209] {approach,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1210] {dataset,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [1211] {approach,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [1212] {propos,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [1213] {approach,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [1214] {network,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [1215] {dataset,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [1216] {propos,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1217] {dataset,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [1218] {network,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1219] {propos,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [1220] {network,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [1221] {reduc,
## time} => {model} 0.1000000 1.0000000 1.875000 3
## [1222] {model,
## time} => {reduc} 0.1000000 0.7500000 3.214286 3
## [1223] {model,
## reduc} => {time} 0.1000000 0.7500000 5.625000 3
## [1224] {show,
## time} => {model} 0.1000000 1.0000000 1.875000 3
## [1225] {model,
## time} => {show} 0.1000000 0.7500000 1.406250 3
## [1226] {model,
## time} => {network} 0.1000000 0.7500000 1.184211 3
## [1227] {network,
## time} => {model} 0.1000000 1.0000000 1.875000 3
## [1228] {simpl,
## studi} => {process} 0.1000000 1.0000000 5.000000 3
## [1229] {process,
## simpl} => {studi} 0.1000000 1.0000000 7.500000 3
## [1230] {process,
## studi} => {simpl} 0.1000000 1.0000000 10.000000 3
## [1231] {simpl,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [1232] {simpl,
## work} => {studi} 0.1000000 1.0000000 7.500000 3
## [1233] {studi,
## work} => {simpl} 0.1000000 1.0000000 10.000000 3
## [1234] {simpl,
## studi} => {network} 0.1000000 1.0000000 1.578947 3
## [1235] {network,
## simpl} => {studi} 0.1000000 1.0000000 7.500000 3
## [1236] {network,
## studi} => {simpl} 0.1000000 1.0000000 10.000000 3
## [1237] {process,
## simpl} => {work} 0.1000000 1.0000000 2.500000 3
## [1238] {simpl,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [1239] {process,
## work} => {simpl} 0.1000000 0.7500000 7.500000 3
## [1240] {process,
## simpl} => {network} 0.1000000 1.0000000 1.578947 3
## [1241] {network,
## simpl} => {process} 0.1000000 1.0000000 5.000000 3
## [1242] {simpl,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [1243] {network,
## simpl} => {work} 0.1000000 1.0000000 2.500000 3
## [1244] {classif,
## baselin} => {propos} 0.1000000 1.0000000 2.000000 3
## [1245] {propos,
## baselin} => {classif} 0.1000000 1.0000000 3.750000 3
## [1246] {result,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [1247] {train,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [1248] {result,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1249] {dataset,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [1250] {result,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1251] {network,
## increas} => {result} 0.1000000 0.7500000 2.250000 3
## [1252] {neural,
## increas} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1253] {algorithm,
## increas} => {neural} 0.1000000 1.0000000 3.000000 3
## [1254] {neural,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1255] {network,
## increas} => {neural} 0.1000000 0.7500000 2.250000 3
## [1256] {algorithm,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1257] {network,
## increas} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1258] {train,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1259] {dataset,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [1260] {train,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1261] {network,
## increas} => {train} 0.1000000 0.7500000 1.875000 3
## [1262] {dataset,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1263] {network,
## increas} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1264] {represent,
## increas} => {featur} 0.1000000 1.0000000 1.875000 3
## [1265] {featur,
## increas} => {represent} 0.1000000 1.0000000 2.000000 3
## [1266] {represent,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1267] {network,
## increas} => {represent} 0.1000000 0.7500000 1.500000 3
## [1268] {featur,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [1269] {network,
## increas} => {featur} 0.1000000 0.7500000 1.406250 3
## [1270] {boltzmann,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [1271] {boltzmann,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1272] {restrict,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1273] {boltzmann,
## restrict} => {machin} 0.1333333 1.0000000 4.285714 4
## [1274] {boltzmann,
## machin} => {restrict} 0.1333333 1.0000000 7.500000 4
## [1275] {machin,
## restrict} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [1276] {boltzmann,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1277] {boltzmann,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1278] {restrict,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1279] {boltzmann,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [1280] {boltzmann,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1281] {restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1282] {boltzmann,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [1283] {boltzmann,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1284] {restrict,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1285] {boltzmann,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [1286] {boltzmann,
## data} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1287] {data,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1288] {boltzmann,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [1289] {boltzmann,
## show} => {restrict} 0.1333333 1.0000000 7.500000 4
## [1290] {restrict,
## show} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [1291] {boltzmann,
## restrict} => {model} 0.1333333 1.0000000 1.875000 4
## [1292] {boltzmann,
## model} => {restrict} 0.1333333 1.0000000 7.500000 4
## [1293] {model,
## restrict} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [1294] {boltzmann,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [1295] {boltzmann,
## featur} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1296] {featur,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1297] {boltzmann,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [1298] {boltzmann,
## machin} => {recent} 0.1000000 0.7500000 3.214286 3
## [1299] {boltzmann,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1300] {boltzmann,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [1301] {algorithm,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [1302] {boltzmann,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [1303] {boltzmann,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [1304] {boltzmann,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [1305] {boltzmann,
## model} => {recent} 0.1000000 0.7500000 3.214286 3
## [1306] {boltzmann,
## machin} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1307] {boltzmann,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [1308] {machin,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1309] {boltzmann,
## machin} => {task} 0.1000000 0.7500000 2.045455 3
## [1310] {boltzmann,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [1311] {machin,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [1312] {boltzmann,
## machin} => {train} 0.1000000 0.7500000 1.875000 3
## [1313] {boltzmann,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [1314] {machin,
## train} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [1315] {boltzmann,
## machin} => {data} 0.1000000 0.7500000 1.730769 3
## [1316] {boltzmann,
## data} => {machin} 0.1000000 1.0000000 4.285714 3
## [1317] {data,
## machin} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [1318] {boltzmann,
## machin} => {show} 0.1333333 1.0000000 1.875000 4
## [1319] {boltzmann,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [1320] {machin,
## show} => {boltzmann} 0.1333333 0.8000000 6.000000 4
## [1321] {boltzmann,
## machin} => {model} 0.1333333 1.0000000 1.875000 4
## [1322] {boltzmann,
## model} => {machin} 0.1333333 1.0000000 4.285714 4
## [1323] {machin,
## model} => {boltzmann} 0.1333333 0.8000000 6.000000 4
## [1324] {boltzmann,
## machin} => {featur} 0.1000000 0.7500000 1.406250 3
## [1325] {boltzmann,
## featur} => {machin} 0.1000000 1.0000000 4.285714 3
## [1326] {boltzmann,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [1327] {boltzmann,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1328] {boltzmann,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [1329] {boltzmann,
## model} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1330] {boltzmann,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [1331] {boltzmann,
## data} => {task} 0.1000000 1.0000000 2.727273 3
## [1332] {boltzmann,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [1333] {boltzmann,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [1334] {boltzmann,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [1335] {boltzmann,
## model} => {task} 0.1000000 0.7500000 2.045455 3
## [1336] {boltzmann,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [1337] {boltzmann,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [1338] {boltzmann,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [1339] {boltzmann,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [1340] {boltzmann,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [1341] {boltzmann,
## model} => {train} 0.1000000 0.7500000 1.875000 3
## [1342] {boltzmann,
## data} => {show} 0.1000000 1.0000000 1.875000 3
## [1343] {boltzmann,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [1344] {boltzmann,
## data} => {model} 0.1000000 1.0000000 1.875000 3
## [1345] {boltzmann,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [1346] {boltzmann,
## data} => {featur} 0.1000000 1.0000000 1.875000 3
## [1347] {boltzmann,
## featur} => {data} 0.1000000 1.0000000 2.307692 3
## [1348] {boltzmann,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [1349] {boltzmann,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [1350] {boltzmann,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [1351] {boltzmann,
## featur} => {show} 0.1000000 1.0000000 1.875000 3
## [1352] {boltzmann,
## model} => {featur} 0.1000000 0.7500000 1.406250 3
## [1353] {boltzmann,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [1354] {creat,
## model} => {network} 0.1000000 1.0000000 1.578947 3
## [1355] {creat,
## network} => {model} 0.1000000 0.7500000 1.406250 3
## [1356] {restrict,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [1357] {machin,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [1358] {restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1359] {restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [1360] {algorithm,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [1361] {restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [1362] {restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [1363] {restrict,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [1364] {model,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [1365] {machin,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1366] {restrict,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [1367] {machin,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1368] {machin,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [1369] {restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [1370] {machin,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [1371] {machin,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [1372] {restrict,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [1373] {machin,
## train} => {restrict} 0.1000000 0.7500000 5.625000 3
## [1374] {machin,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [1375] {data,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [1376] {data,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [1377] {machin,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [1378] {restrict,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [1379] {machin,
## show} => {restrict} 0.1333333 0.8000000 6.000000 4
## [1380] {machin,
## restrict} => {model} 0.1333333 1.0000000 1.875000 4
## [1381] {model,
## restrict} => {machin} 0.1333333 1.0000000 4.285714 4
## [1382] {machin,
## model} => {restrict} 0.1333333 0.8000000 6.000000 4
## [1383] {machin,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [1384] {featur,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [1385] {restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [1386] {restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1387] {restrict,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [1388] {model,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [1389] {restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [1390] {data,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [1391] {restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [1392] {restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [1393] {restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [1394] {model,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [1395] {restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [1396] {featur,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [1397] {restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [1398] {restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [1399] {restrict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [1400] {model,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [1401] {data,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [1402] {restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [1403] {data,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [1404] {model,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [1405] {data,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [1406] {featur,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [1407] {restrict,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [1408] {model,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [1409] {restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [1410] {featur,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [1411] {model,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [1412] {featur,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [1413] {dataset,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [1414] {learn,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1415] {dataset,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [1416] {propos,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1417] {dataset,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [1418] {model,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1419] {learn,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [1420] {propos,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [1421] {learn,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [1422] {model,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [1423] {propos,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [1424] {model,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [1425] {make,
## present} => {model} 0.1000000 1.0000000 1.875000 3
## [1426] {model,
## present} => {make} 0.1000000 1.0000000 3.333333 3
## [1427] {data,
## present} => {show} 0.1000000 1.0000000 1.875000 3
## [1428] {present,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [1429] {neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1430] {algorithm,
## order} => {neural} 0.1000000 0.7500000 2.250000 3
## [1431] {neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [1432] {approach,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [1433] {neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1434] {model,
## order} => {neural} 0.1000000 0.7500000 2.250000 3
## [1435] {neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [1436] {network,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [1437] {algorithm,
## order} => {approach} 0.1000000 0.7500000 1.875000 3
## [1438] {approach,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1439] {algorithm,
## order} => {show} 0.1000000 0.7500000 1.406250 3
## [1440] {show,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1441] {algorithm,
## order} => {propos} 0.1000000 0.7500000 1.500000 3
## [1442] {order,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1443] {algorithm,
## order} => {model} 0.1333333 1.0000000 1.875000 4
## [1444] {model,
## order} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [1445] {algorithm,
## order} => {featur} 0.1000000 0.7500000 1.406250 3
## [1446] {featur,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1447] {algorithm,
## order} => {network} 0.1000000 0.7500000 1.184211 3
## [1448] {network,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1449] {approach,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1450] {model,
## order} => {approach} 0.1000000 0.7500000 1.875000 3
## [1451] {approach,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [1452] {network,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [1453] {show,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1454] {model,
## order} => {show} 0.1000000 0.7500000 1.406250 3
## [1455] {show,
## order} => {featur} 0.1000000 1.0000000 1.875000 3
## [1456] {featur,
## order} => {show} 0.1000000 1.0000000 1.875000 3
## [1457] {order,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [1458] {model,
## order} => {propos} 0.1000000 0.7500000 1.500000 3
## [1459] {model,
## order} => {featur} 0.1000000 0.7500000 1.406250 3
## [1460] {featur,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1461] {model,
## order} => {network} 0.1000000 0.7500000 1.184211 3
## [1462] {network,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [1463] {power,
## specif} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1464] {algorithm,
## power} => {specif} 0.1000000 1.0000000 6.000000 3
## [1465] {algorithm,
## specif} => {power} 0.1000000 0.7500000 5.625000 3
## [1466] {power,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [1467] {train,
## power} => {specif} 0.1000000 1.0000000 6.000000 3
## [1468] {improv,
## power} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1469] {dataset,
## power} => {improv} 0.1000000 1.0000000 3.333333 3
## [1470] {algorithm,
## power} => {train} 0.1000000 1.0000000 2.500000 3
## [1471] {train,
## power} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [1472] {perform,
## power} => {featur} 0.1000000 1.0000000 1.875000 3
## [1473] {featur,
## power} => {perform} 0.1000000 1.0000000 2.142857 3
## [1474] {demonstr,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1475] {dataset,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [1476] {dataset,
## demonstr} => {benchmark} 0.1000000 0.7500000 5.625000 3
## [1477] {demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1478] {learn,
## benchmark} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [1479] {demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [1480] {demonstr,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [1481] {model,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [1482] {model,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [1483] {demonstr,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1484] {featur,
## benchmark} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [1485] {featur,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [1486] {classif,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1487] {learn,
## benchmark} => {classif} 0.1000000 0.7500000 2.812500 3
## [1488] {classif,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1489] {featur,
## benchmark} => {classif} 0.1000000 0.7500000 2.812500 3
## [1490] {problem,
## benchmark} => {perform} 0.1000000 1.0000000 2.142857 3
## [1491] {perform,
## benchmark} => {problem} 0.1000000 1.0000000 3.333333 3
## [1492] {problem,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1493] {learn,
## benchmark} => {problem} 0.1000000 0.7500000 2.500000 3
## [1494] {problem,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1495] {featur,
## benchmark} => {problem} 0.1000000 0.7500000 2.500000 3
## [1496] {perform,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1497] {learn,
## benchmark} => {perform} 0.1000000 0.7500000 1.607143 3
## [1498] {perform,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1499] {featur,
## benchmark} => {perform} 0.1000000 0.7500000 1.607143 3
## [1500] {dataset,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1501] {learn,
## benchmark} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1502] {dataset,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [1503] {model,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1504] {dataset,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1505] {featur,
## benchmark} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1506] {learn,
## benchmark} => {propos} 0.1000000 0.7500000 1.500000 3
## [1507] {propos,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1508] {learn,
## benchmark} => {model} 0.1000000 0.7500000 1.406250 3
## [1509] {model,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [1510] {learn,
## benchmark} => {featur} 0.1333333 1.0000000 1.875000 4
## [1511] {featur,
## benchmark} => {learn} 0.1333333 1.0000000 2.307692 4
## [1512] {propos,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1513] {featur,
## benchmark} => {propos} 0.1000000 0.7500000 1.500000 3
## [1514] {model,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [1515] {featur,
## benchmark} => {model} 0.1000000 0.7500000 1.406250 3
## [1516] {success,
## high} => {represent} 0.1000000 1.0000000 2.000000 3
## [1517] {represent,
## high} => {success} 0.1000000 1.0000000 3.750000 3
## [1518] {success,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1519] {propos,
## high} => {success} 0.1000000 0.7500000 2.812500 3
## [1520] {success,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1521] {featur,
## high} => {success} 0.1000000 0.7500000 2.812500 3
## [1522] {object,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1523] {propos,
## high} => {object} 0.1000000 0.7500000 2.812500 3
## [1524] {object,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1525] {featur,
## high} => {object} 0.1000000 0.7500000 2.812500 3
## [1526] {classif,
## high} => {learn} 0.1000000 1.0000000 2.307692 3
## [1527] {learn,
## high} => {classif} 0.1000000 1.0000000 3.750000 3
## [1528] {classif,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1529] {propos,
## high} => {classif} 0.1000000 0.7500000 2.812500 3
## [1530] {classif,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1531] {featur,
## high} => {classif} 0.1000000 0.7500000 2.812500 3
## [1532] {task,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1533] {propos,
## high} => {task} 0.1000000 0.7500000 2.045455 3
## [1534] {task,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1535] {featur,
## high} => {task} 0.1000000 0.7500000 2.045455 3
## [1536] {learn,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1537] {propos,
## high} => {learn} 0.1000000 0.7500000 1.730769 3
## [1538] {learn,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1539] {featur,
## high} => {learn} 0.1000000 0.7500000 1.730769 3
## [1540] {represent,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [1541] {propos,
## high} => {represent} 0.1000000 0.7500000 1.500000 3
## [1542] {represent,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [1543] {featur,
## high} => {represent} 0.1000000 0.7500000 1.500000 3
## [1544] {propos,
## high} => {featur} 0.1333333 1.0000000 1.875000 4
## [1545] {featur,
## high} => {propos} 0.1333333 1.0000000 2.000000 4
## [1546] {parallel,
## framework} => {network} 0.1000000 1.0000000 1.578947 3
## [1547] {network,
## parallel} => {framework} 0.1000000 0.7500000 3.750000 3
## [1548] {network,
## framework} => {parallel} 0.1000000 0.7500000 5.625000 3
## [1549] {comput,
## parallel} => {reduc} 0.1000000 1.0000000 4.285714 3
## [1550] {reduc,
## parallel} => {comput} 0.1000000 1.0000000 4.285714 3
## [1551] {reduc,
## comput} => {parallel} 0.1000000 0.7500000 5.625000 3
## [1552] {comput,
## parallel} => {network} 0.1000000 1.0000000 1.578947 3
## [1553] {network,
## parallel} => {comput} 0.1000000 0.7500000 3.214286 3
## [1554] {reduc,
## parallel} => {network} 0.1000000 1.0000000 1.578947 3
## [1555] {network,
## parallel} => {reduc} 0.1000000 0.7500000 3.214286 3
## [1556] {parallel,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [1557] {network,
## parallel} => {work} 0.1000000 0.7500000 1.875000 3
## [1558] {outperform,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1559] {predict,
## train} => {outperform} 0.1000000 0.7500000 5.625000 3
## [1560] {outperform,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [1561] {outperform,
## predict} => {show} 0.1000000 1.0000000 1.875000 3
## [1562] {predict,
## show} => {outperform} 0.1000000 1.0000000 7.500000 3
## [1563] {outperform,
## show} => {predict} 0.1000000 0.7500000 5.625000 3
## [1564] {predict,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [1565] {predict,
## train} => {challeng} 0.1000000 0.7500000 4.500000 3
## [1566] {train,
## challeng} => {predict} 0.1000000 0.7500000 5.625000 3
## [1567] {predict,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [1568] {predict,
## perform} => {challeng} 0.1000000 1.0000000 6.000000 3
## [1569] {perform,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [1570] {predict,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [1571] {predict,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [1572] {propos,
## challeng} => {predict} 0.1000000 0.7500000 5.625000 3
## [1573] {paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1574] {predict,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [1575] {paper,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [1576] {data,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [1577] {paper,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [1578] {model,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [1579] {paper,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [1580] {featur,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [1581] {predict,
## train} => {perform} 0.1000000 0.7500000 1.607143 3
## [1582] {predict,
## perform} => {train} 0.1000000 1.0000000 2.500000 3
## [1583] {predict,
## train} => {data} 0.1000000 0.7500000 1.730769 3
## [1584] {data,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1585] {predict,
## train} => {represent} 0.1000000 0.7500000 1.500000 3
## [1586] {predict,
## represent} => {train} 0.1000000 1.0000000 2.500000 3
## [1587] {predict,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [1588] {predict,
## show} => {train} 0.1000000 1.0000000 2.500000 3
## [1589] {predict,
## train} => {propos} 0.1000000 0.7500000 1.500000 3
## [1590] {predict,
## propos} => {train} 0.1000000 1.0000000 2.500000 3
## [1591] {predict,
## train} => {model} 0.1000000 0.7500000 1.406250 3
## [1592] {model,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1593] {predict,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [1594] {featur,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [1595] {predict,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [1596] {predict,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [1597] {data,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [1598] {model,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [1599] {data,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [1600] {featur,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [1601] {model,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [1602] {featur,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [1603] {number,
## applic} => {perform} 0.1000000 1.0000000 2.142857 3
## [1604] {number,
## perform} => {applic} 0.1000000 1.0000000 4.285714 3
## [1605] {make,
## number} => {data} 0.1000000 1.0000000 2.307692 3
## [1606] {data,
## number} => {make} 0.1000000 1.0000000 3.333333 3
## [1607] {make,
## number} => {model} 0.1000000 1.0000000 1.875000 3
## [1608] {model,
## number} => {make} 0.1000000 0.7500000 2.500000 3
## [1609] {number,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [1610] {model,
## number} => {result} 0.1000000 0.7500000 2.250000 3
## [1611] {method,
## number} => {show} 0.1000000 1.0000000 1.875000 3
## [1612] {number,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [1613] {approach,
## number} => {model} 0.1000000 1.0000000 1.875000 3
## [1614] {model,
## number} => {approach} 0.1000000 0.7500000 1.875000 3
## [1615] {data,
## number} => {model} 0.1000000 1.0000000 1.875000 3
## [1616] {model,
## number} => {data} 0.1000000 0.7500000 1.730769 3
## [1617] {number,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [1618] {featur,
## number} => {represent} 0.1000000 1.0000000 2.000000 3
## [1619] {face,
## variat} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1620] {recognit,
## face} => {variat} 0.1000000 0.7500000 5.625000 3
## [1621] {recognit,
## variat} => {face} 0.1000000 0.7500000 5.625000 3
## [1622] {challeng,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1623] {recognit,
## face} => {challeng} 0.1000000 0.7500000 4.500000 3
## [1624] {recognit,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [1625] {challeng,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [1626] {train,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [1627] {train,
## challeng} => {face} 0.1000000 0.7500000 5.625000 3
## [1628] {challeng,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [1629] {represent,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [1630] {represent,
## challeng} => {face} 0.1000000 0.7500000 5.625000 3
## [1631] {imag,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1632] {recognit,
## face} => {imag} 0.1000000 0.7500000 4.500000 3
## [1633] {recognit,
## imag} => {face} 0.1000000 0.7500000 5.625000 3
## [1634] {imag,
## face} => {propos} 0.1000000 1.0000000 2.000000 3
## [1635] {propos,
## face} => {imag} 0.1000000 1.0000000 6.000000 3
## [1636] {recognit,
## face} => {train} 0.1000000 0.7500000 1.875000 3
## [1637] {train,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1638] {train,
## recognit} => {face} 0.1000000 0.7500000 5.625000 3
## [1639] {recognit,
## face} => {data} 0.1000000 0.7500000 1.730769 3
## [1640] {data,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1641] {recognit,
## face} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1642] {dataset,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1643] {recognit,
## face} => {learn} 0.1000000 0.7500000 1.730769 3
## [1644] {learn,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1645] {recognit,
## face} => {represent} 0.1000000 0.7500000 1.500000 3
## [1646] {represent,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1647] {recognit,
## face} => {propos} 0.1000000 0.7500000 1.500000 3
## [1648] {propos,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1649] {recognit,
## face} => {featur} 0.1000000 0.7500000 1.406250 3
## [1650] {featur,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [1651] {train,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [1652] {represent,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [1653] {data,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1654] {dataset,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [1655] {data,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [1656] {learn,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [1657] {data,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [1658] {featur,
## face} => {data} 0.1000000 1.0000000 2.307692 3
## [1659] {dataset,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [1660] {learn,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1661] {dataset,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [1662] {featur,
## face} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1663] {learn,
## face} => {featur} 0.1000000 1.0000000 1.875000 3
## [1664] {featur,
## face} => {learn} 0.1000000 1.0000000 2.307692 3
## [1665] {method,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [1666] {approach,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [1667] {method,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1668] {dataset,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [1669] {method,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1670] {learn,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [1671] {method,
## sourc} => {show} 0.1000000 1.0000000 1.875000 3
## [1672] {show,
## sourc} => {method} 0.1000000 0.7500000 2.045455 3
## [1673] {method,
## sourc} => {model} 0.1000000 1.0000000 1.875000 3
## [1674] {model,
## sourc} => {method} 0.1000000 1.0000000 2.727273 3
## [1675] {approach,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [1676] {dataset,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [1677] {approach,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [1678] {learn,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [1679] {approach,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [1680] {represent,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [1681] {approach,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [1682] {show,
## sourc} => {approach} 0.1333333 1.0000000 2.500000 4
## [1683] {approach,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [1684] {propos,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [1685] {approach,
## sourc} => {model} 0.1000000 0.7500000 1.406250 3
## [1686] {model,
## sourc} => {approach} 0.1000000 1.0000000 2.500000 3
## [1687] {dataset,
## sourc} => {learn} 0.1333333 1.0000000 2.307692 4
## [1688] {learn,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [1689] {dataset,
## sourc} => {represent} 0.1000000 0.7500000 1.500000 3
## [1690] {represent,
## sourc} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1691] {dataset,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [1692] {show,
## sourc} => {dataset} 0.1333333 1.0000000 2.307692 4
## [1693] {dataset,
## sourc} => {propos} 0.1000000 0.7500000 1.500000 3
## [1694] {propos,
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## [1695] {dataset,
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## [1696] {model,
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## [1697] {learn,
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## [1698] {represent,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1699] {learn,
## sourc} => {show} 0.1333333 1.0000000 1.875000 4
## [1700] {show,
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## [1701] {learn,
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## [1702] {propos,
## sourc} => {learn} 0.1000000 1.0000000 2.307692 3
## [1703] {learn,
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## [1704] {model,
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## [1705] {represent,
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## [1706] {show,
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## [1707] {represent,
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## [1708] {propos,
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## [1709] {show,
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## [1710] {propos,
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## [1711] {show,
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## [1712] {model,
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## [1714] {recognit,
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## [1715] {general,
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## [1716] {general,
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## [1718] {represent,
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## [1719] {general,
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## [1720] {show,
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## [1721] {recognit,
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## [1722] {task,
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## [1723] {task,
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## [1724] {recognit,
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## [1725] {data,
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## [1726] {recognit,
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## [1727] {learn,
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## [1728] {recognit,
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## [1729] {represent,
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## [1730] {recognit,
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## [1731] {show,
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## [1732] {recognit,
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## [1733] {featur,
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## [1735] {data,
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## [1736] {task,
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## [1737] {learn,
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## [1738] {task,
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## [1739] {featur,
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## [1740] {data,
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## [1741] {learn,
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## [1742] {data,
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## [1743] {featur,
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## [1745] {featur,
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## [1746] {represent,
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## [1747] {show,
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## [1748] {process,
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## [1750] {process,
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## [1751] {process,
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## [1752] {model,
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## [1753] {model,
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## [1754] {process,
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## [1755] {featur,
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## [1757] {data,
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## [1758] {introduc,
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## [1760] {introduc,
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## [1761] {featur,
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## [1762] {model,
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## [1763] {featur,
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## [1765] {approach,
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## [1766] {import,
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## [1767] {data,
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## [1768] {import,
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## [1769] {import,
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## [1770] {import,
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## [1772] {import,
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## [1773] {import,
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## [1774] {import,
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## [1776] {import,
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## [1777] {featur,
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## [1778] {approach,
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## [1779] {data,
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## [1780] {approach,
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## [1782] {approach,
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## [1783] {import,
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## [1785] {featur,
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## [1824] {data,
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## [1825] {data,
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## [1826] {art,
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## [1827] {dataset,
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## [1829] {art,
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## [1830] {learn,
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## [1947] {joint,
## propos} => {general} 0.1000000 0.7500000 3.750000 3
## [1948] {general,
## propos} => {joint} 0.1000000 0.7500000 5.625000 3
## [1949] {joint,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [1950] {show,
## joint} => {object} 0.1000000 0.7500000 2.812500 3
## [1951] {joint,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [1952] {joint,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [1953] {method,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [1954] {approach,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [1955] {method,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1956] {dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [1957] {method,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1958] {show,
## joint} => {method} 0.1000000 0.7500000 2.045455 3
## [1959] {method,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1960] {joint,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [1961] {method,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [1962] {model,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [1963] {approach,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1964] {dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [1965] {approach,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1966] {show,
## joint} => {approach} 0.1000000 0.7500000 1.875000 3
## [1967] {approach,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1968] {joint,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [1969] {approach,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [1970] {model,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [1971] {joint,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [1972] {show,
## joint} => {perform} 0.1000000 0.7500000 1.607143 3
## [1973] {joint,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [1974] {joint,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [1975] {dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1976] {show,
## joint} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1977] {dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1978] {joint,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [1979] {dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [1980] {model,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [1981] {represent,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1982] {show,
## joint} => {represent} 0.1000000 0.7500000 1.500000 3
## [1983] {represent,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1984] {joint,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [1985] {show,
## joint} => {propos} 0.1333333 1.0000000 2.000000 4
## [1986] {joint,
## propos} => {show} 0.1333333 1.0000000 1.875000 4
## [1987] {show,
## joint} => {model} 0.1000000 0.7500000 1.406250 3
## [1988] {model,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [1989] {joint,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [1990] {model,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [1991] {addit,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [1992] {imag,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [1993] {addit,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [1994] {addit,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [1995] {classif,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [1996] {classif,
## addit} => {studi} 0.1000000 1.0000000 7.500000 3
## [1997] {addit,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [1998] {propos,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [1999] {imag,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [2000] {classif,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [2001] {classif,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [2002] {imag,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [2003] {propos,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [2004] {process,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [2005] {studi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [2006] {process,
## work} => {studi} 0.1000000 0.7500000 5.625000 3
## [2007] {process,
## studi} => {network} 0.1000000 1.0000000 1.578947 3
## [2008] {network,
## studi} => {process} 0.1000000 1.0000000 5.000000 3
## [2009] {classif,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [2010] {propos,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [2011] {studi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [2012] {network,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [2013] {studi,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [2014] {featur,
## studi} => {learn} 0.1000000 1.0000000 2.307692 3
## [2015] {evalu,
## make} => {paper} 0.1000000 1.0000000 3.000000 3
## [2016] {evalu,
## paper} => {make} 0.1000000 0.7500000 2.500000 3
## [2017] {evalu,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [2018] {evalu,
## method} => {make} 0.1000000 0.7500000 2.500000 3
## [2019] {evalu,
## paper} => {method} 0.1000000 0.7500000 2.045455 3
## [2020] {evalu,
## method} => {paper} 0.1000000 0.7500000 2.250000 3
## [2021] {method,
## paper} => {evalu} 0.1000000 0.7500000 4.500000 3
## [2022] {evalu,
## paper} => {represent} 0.1000000 0.7500000 1.500000 3
## [2023] {evalu,
## represent} => {paper} 0.1000000 0.7500000 2.250000 3
## [2024] {evalu,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [2025] {evalu,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [2026] {evalu,
## paper} => {model} 0.1000000 0.7500000 1.406250 3
## [2027] {evalu,
## model} => {paper} 0.1000000 0.7500000 2.250000 3
## [2028] {evalu,
## paper} => {network} 0.1000000 0.7500000 1.184211 3
## [2029] {evalu,
## network} => {paper} 0.1000000 0.7500000 2.250000 3
## [2030] {evalu,
## method} => {task} 0.1000000 0.7500000 2.045455 3
## [2031] {evalu,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [2032] {method,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [2033] {evalu,
## method} => {approach} 0.1000000 0.7500000 1.875000 3
## [2034] {approach,
## evalu} => {method} 0.1000000 1.0000000 2.727273 3
## [2035] {evalu,
## method} => {data} 0.1000000 0.7500000 1.730769 3
## [2036] {data,
## evalu} => {method} 0.1000000 1.0000000 2.727273 3
## [2037] {data,
## method} => {evalu} 0.1000000 1.0000000 6.000000 3
## [2038] {evalu,
## method} => {represent} 0.1000000 0.7500000 1.500000 3
## [2039] {evalu,
## represent} => {method} 0.1000000 0.7500000 2.045455 3
## [2040] {evalu,
## method} => {show} 0.1000000 0.7500000 1.406250 3
## [2041] {evalu,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [2042] {evalu,
## method} => {model} 0.1000000 0.7500000 1.406250 3
## [2043] {evalu,
## model} => {method} 0.1000000 0.7500000 2.045455 3
## [2044] {evalu,
## method} => {featur} 0.1000000 0.7500000 1.406250 3
## [2045] {evalu,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [2046] {evalu,
## method} => {network} 0.1000000 0.7500000 1.184211 3
## [2047] {evalu,
## network} => {method} 0.1000000 0.7500000 2.045455 3
## [2048] {evalu,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [2049] {approach,
## evalu} => {task} 0.1000000 1.0000000 2.727273 3
## [2050] {evalu,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [2051] {evalu,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [2052] {evalu,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [2053] {evalu,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [2054] {evalu,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [2055] {evalu,
## network} => {task} 0.1000000 0.7500000 2.045455 3
## [2056] {approach,
## evalu} => {represent} 0.1000000 1.0000000 2.000000 3
## [2057] {evalu,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [2058] {approach,
## evalu} => {featur} 0.1000000 1.0000000 1.875000 3
## [2059] {evalu,
## featur} => {approach} 0.1000000 1.0000000 2.500000 3
## [2060] {approach,
## evalu} => {network} 0.1000000 1.0000000 1.578947 3
## [2061] {evalu,
## network} => {approach} 0.1000000 0.7500000 1.875000 3
## [2062] {data,
## evalu} => {show} 0.1000000 1.0000000 1.875000 3
## [2063] {evalu,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [2064] {data,
## evalu} => {model} 0.1000000 1.0000000 1.875000 3
## [2065] {evalu,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [2066] {evalu,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [2067] {evalu,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [2068] {evalu,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [2069] {evalu,
## model} => {represent} 0.1000000 0.7500000 1.500000 3
## [2070] {evalu,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [2071] {evalu,
## featur} => {represent} 0.1000000 1.0000000 2.000000 3
## [2072] {evalu,
## represent} => {network} 0.1333333 1.0000000 1.578947 4
## [2073] {evalu,
## network} => {represent} 0.1333333 1.0000000 2.000000 4
## [2074] {evalu,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [2075] {evalu,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [2076] {evalu,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [2077] {evalu,
## network} => {show} 0.1000000 0.7500000 1.406250 3
## [2078] {evalu,
## model} => {network} 0.1000000 0.7500000 1.184211 3
## [2079] {evalu,
## network} => {model} 0.1000000 0.7500000 1.406250 3
## [2080] {evalu,
## featur} => {network} 0.1000000 1.0000000 1.578947 3
## [2081] {evalu,
## network} => {featur} 0.1000000 0.7500000 1.406250 3
## [2082] {outperform,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [2083] {outperform,
## object} => {challeng} 0.1000000 1.0000000 6.000000 3
## [2084] {object,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [2085] {outperform,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [2086] {outperform,
## show} => {challeng} 0.1000000 0.7500000 4.500000 3
## [2087] {show,
## challeng} => {outperform} 0.1000000 0.7500000 5.625000 3
## [2088] {outperform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [2089] {outperform,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [2090] {propos,
## challeng} => {outperform} 0.1000000 0.7500000 5.625000 3
## [2091] {outperform,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [2092] {outperform,
## show} => {object} 0.1000000 0.7500000 2.812500 3
## [2093] {outperform,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [2094] {outperform,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [2095] {outperform,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [2096] {data,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [2097] {outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [2098] {outperform,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [2099] {outperform,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [2100] {model,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [2101] {outperform,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [2102] {featur,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [2103] {outperform,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [2104] {outperform,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [2105] {data,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [2106] {outperform,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [2107] {data,
## outperform} => {model} 0.1000000 1.0000000 1.875000 3
## [2108] {model,
## outperform} => {data} 0.1000000 1.0000000 2.307692 3
## [2109] {data,
## outperform} => {featur} 0.1000000 1.0000000 1.875000 3
## [2110] {featur,
## outperform} => {data} 0.1000000 1.0000000 2.307692 3
## [2111] {outperform,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [2112] {outperform,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [2113] {outperform,
## show} => {propos} 0.1000000 0.7500000 1.500000 3
## [2114] {outperform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [2115] {outperform,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [2116] {model,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [2117] {outperform,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [2118] {featur,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [2119] {model,
## outperform} => {featur} 0.1000000 1.0000000 1.875000 3
## [2120] {featur,
## outperform} => {model} 0.1000000 1.0000000 1.875000 3
## [2121] {task,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [2122] {data,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [2123] {task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2124] {dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [2125] {task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [2126] {learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [2127] {task,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [2128] {featur,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [2129] {task,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2130] {network,
## design} => {task} 0.1000000 0.7500000 2.045455 3
## [2131] {work,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2132] {network,
## design} => {work} 0.1000000 0.7500000 1.875000 3
## [2133] {data,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2134] {dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [2135] {data,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [2136] {learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [2137] {data,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [2138] {featur,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [2139] {data,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2140] {network,
## design} => {data} 0.1000000 0.7500000 1.730769 3
## [2141] {dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [2142] {learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2143] {dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [2144] {featur,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2145] {dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2146] {network,
## design} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2147] {learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [2148] {featur,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [2149] {learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2150] {network,
## design} => {learn} 0.1000000 0.7500000 1.730769 3
## [2151] {featur,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [2152] {network,
## design} => {featur} 0.1000000 0.7500000 1.406250 3
## [2153] {general,
## system} => {show} 0.1000000 1.0000000 1.875000 3
## [2154] {show,
## system} => {general} 0.1000000 1.0000000 5.000000 3
## [2155] {general,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [2156] {model,
## system} => {general} 0.1000000 0.7500000 3.750000 3
## [2157] {model,
## general} => {system} 0.1000000 0.7500000 4.500000 3
## [2158] {architectur,
## system} => {featur} 0.1000000 1.0000000 1.875000 3
## [2159] {featur,
## system} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2160] {recognit,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [2161] {model,
## system} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2162] {model,
## recognit} => {system} 0.1000000 0.7500000 4.500000 3
## [2163] {recognit,
## system} => {featur} 0.1000000 1.0000000 1.875000 3
## [2164] {featur,
## system} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2165] {method,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [2166] {perform,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [2167] {method,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2168] {dataset,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [2169] {method,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [2170] {propos,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [2171] {perform,
## system} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2172] {dataset,
## system} => {perform} 0.1333333 1.0000000 2.142857 4
## [2173] {perform,
## system} => {propos} 0.1333333 1.0000000 2.000000 4
## [2174] {propos,
## system} => {perform} 0.1333333 1.0000000 2.142857 4
## [2175] {perform,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [2176] {model,
## system} => {perform} 0.1000000 0.7500000 1.607143 3
## [2177] {perform,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [2178] {featur,
## system} => {perform} 0.1000000 0.7500000 1.607143 3
## [2179] {dataset,
## system} => {propos} 0.1333333 1.0000000 2.000000 4
## [2180] {propos,
## system} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2181] {dataset,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [2182] {model,
## system} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2183] {dataset,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [2184] {featur,
## system} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2185] {system,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [2186] {represent,
## system} => {learn} 0.1000000 1.0000000 2.307692 3
## [2187] {system,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [2188] {model,
## system} => {learn} 0.1000000 0.7500000 1.730769 3
## [2189] {represent,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [2190] {model,
## system} => {represent} 0.1000000 0.7500000 1.500000 3
## [2191] {show,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [2192] {model,
## system} => {show} 0.1000000 0.7500000 1.406250 3
## [2193] {propos,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [2194] {model,
## system} => {propos} 0.1000000 0.7500000 1.500000 3
## [2195] {propos,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [2196] {featur,
## system} => {propos} 0.1000000 0.7500000 1.500000 3
## [2197] {model,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [2198] {featur,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [2199] {detect,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [2200] {method,
## detect} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [2201] {method,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [2202] {detect,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2203] {detect,
## propos} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [2204] {detect,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [2205] {featur,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [2206] {appli,
## detect} => {method} 0.1000000 1.0000000 2.727273 3
## [2207] {method,
## detect} => {appli} 0.1000000 0.7500000 3.750000 3
## [2208] {method,
## appli} => {detect} 0.1000000 0.7500000 4.500000 3
## [2209] {appli,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [2210] {detect,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [2211] {appli,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [2212] {detect,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [2213] {appli,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2214] {featur,
## appli} => {detect} 0.1000000 0.7500000 4.500000 3
## [2215] {detect,
## object} => {method} 0.1000000 1.0000000 2.727273 3
## [2216] {method,
## detect} => {object} 0.1000000 0.7500000 2.812500 3
## [2217] {method,
## object} => {detect} 0.1000000 0.7500000 4.500000 3
## [2218] {detect,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [2219] {show,
## detect} => {object} 0.1000000 1.0000000 3.750000 3
## [2220] {detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [2221] {detect,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [2222] {detect,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [2223] {architectur,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [2224] {detect,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2225] {architectur,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2226] {dataset,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2227] {architectur,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2228] {architectur,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [2229] {network,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2230] {method,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [2231] {detect,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [2232] {method,
## detect} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2233] {dataset,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [2234] {method,
## detect} => {show} 0.1000000 0.7500000 1.406250 3
## [2235] {show,
## detect} => {method} 0.1000000 1.0000000 2.727273 3
## [2236] {method,
## detect} => {propos} 0.1333333 1.0000000 2.000000 4
## [2237] {detect,
## propos} => {method} 0.1333333 1.0000000 2.727273 4
## [2238] {method,
## detect} => {featur} 0.1333333 1.0000000 1.875000 4
## [2239] {featur,
## detect} => {method} 0.1333333 0.8000000 2.181818 4
## [2240] {method,
## detect} => {network} 0.1000000 0.7500000 1.184211 3
## [2241] {network,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [2242] {detect,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2243] {dataset,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [2244] {detect,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [2245] {detect,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [2246] {network,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [2247] {detect,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2248] {dataset,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [2249] {detect,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [2250] {detect,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [2251] {detect,
## perform} => {featur} 0.1333333 1.0000000 1.875000 4
## [2252] {featur,
## detect} => {perform} 0.1333333 0.8000000 1.714286 4
## [2253] {detect,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [2254] {network,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [2255] {dataset,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [2256] {detect,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2257] {dataset,
## detect} => {featur} 0.1333333 1.0000000 1.875000 4
## [2258] {featur,
## detect} => {dataset} 0.1333333 0.8000000 1.846154 4
## [2259] {dataset,
## detect} => {network} 0.1333333 1.0000000 1.578947 4
## [2260] {network,
## detect} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2261] {represent,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2262] {show,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [2263] {detect,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [2264] {show,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [2265] {detect,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [2266] {featur,
## detect} => {propos} 0.1333333 0.8000000 1.600000 4
## [2267] {detect,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [2268] {network,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [2269] {featur,
## detect} => {network} 0.1333333 0.8000000 1.263158 4
## [2270] {network,
## detect} => {featur} 0.1333333 1.0000000 1.875000 4
## [2271] {object,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [2272] {represent,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [2273] {represent,
## object} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [2274] {object,
## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [2275] {show,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [2276] {object,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2277] {method,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2278] {method,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [2279] {featur,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [2280] {work,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2281] {perform,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2282] {dataset,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2283] {dataset,
## stateoftheart} => {network} 0.1000000 1.0000000 1.578947 3
## [2284] {network,
## stateoftheart} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2285] {represent,
## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [2286] {show,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [2287] {represent,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2288] {show,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2289] {featur,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2290] {network,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [2291] {addit,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2292] {architectur,
## effici} => {addit} 0.1000000 0.7500000 4.500000 3
## [2293] {architectur,
## addit} => {effici} 0.1000000 1.0000000 6.000000 3
## [2294] {addit,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [2295] {effici,
## work} => {addit} 0.1000000 0.7500000 4.500000 3
## [2296] {addit,
## work} => {effici} 0.1000000 0.7500000 4.500000 3
## [2297] {addit,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2298] {dataset,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [2299] {dataset,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [2300] {addit,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [2301] {propos,
## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [2302] {addit,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [2303] {network,
## effici} => {addit} 0.1000000 0.7500000 4.500000 3
## [2304] {network,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [2305] {comput,
## effici} => {reduc} 0.1000000 1.0000000 4.285714 3
## [2306] {reduc,
## effici} => {comput} 0.1000000 1.0000000 4.285714 3
## [2307] {reduc,
## comput} => {effici} 0.1000000 0.7500000 4.500000 3
## [2308] {comput,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [2309] {effici,
## optim} => {comput} 0.1000000 1.0000000 4.285714 3
## [2310] {comput,
## optim} => {effici} 0.1000000 1.0000000 6.000000 3
## [2311] {comput,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [2312] {effici,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [2313] {comput,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [2314] {comput,
## effici} => {improv} 0.1000000 1.0000000 3.333333 3
## [2315] {improv,
## effici} => {comput} 0.1000000 1.0000000 4.285714 3
## [2316] {improv,
## comput} => {effici} 0.1000000 0.7500000 4.500000 3
## [2317] {reduc,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [2318] {effici,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [2319] {reduc,
## optim} => {effici} 0.1000000 0.7500000 4.500000 3
## [2320] {reduc,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [2321] {effici,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [2322] {reduc,
## problem} => {effici} 0.1000000 1.0000000 6.000000 3
## [2323] {reduc,
## effici} => {improv} 0.1000000 1.0000000 3.333333 3
## [2324] {improv,
## effici} => {reduc} 0.1000000 1.0000000 4.285714 3
## [2325] {reduc,
## improv} => {effici} 0.1000000 1.0000000 6.000000 3
## [2326] {effici,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [2327] {effici,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [2328] {effici,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [2329] {improv,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [2330] {improv,
## optim} => {effici} 0.1000000 0.7500000 4.500000 3
## [2331] {architectur,
## effici} => {work} 0.1333333 1.0000000 2.500000 4
## [2332] {effici,
## work} => {architectur} 0.1333333 1.0000000 3.750000 4
## [2333] {architectur,
## work} => {effici} 0.1333333 0.8000000 4.800000 4
## [2334] {architectur,
## effici} => {perform} 0.1000000 0.7500000 1.607143 3
## [2335] {perform,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2336] {architectur,
## effici} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2337] {dataset,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2338] {architectur,
## effici} => {propos} 0.1000000 0.7500000 1.500000 3
## [2339] {propos,
## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2340] {architectur,
## effici} => {network} 0.1333333 1.0000000 1.578947 4
## [2341] {network,
## effici} => {architectur} 0.1333333 1.0000000 3.750000 4
## [2342] {effici,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [2343] {improv,
## effici} => {problem} 0.1000000 1.0000000 3.333333 3
## [2344] {improv,
## problem} => {effici} 0.1000000 0.7500000 4.500000 3
## [2345] {effici,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [2346] {perform,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [2347] {effici,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2348] {dataset,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [2349] {effici,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [2350] {propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [2351] {effici,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [2352] {network,
## effici} => {work} 0.1333333 1.0000000 2.500000 4
## [2353] {perform,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [2354] {network,
## effici} => {perform} 0.1000000 0.7500000 1.607143 3
## [2355] {dataset,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [2356] {propos,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2357] {dataset,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [2358] {network,
## effici} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2359] {propos,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [2360] {network,
## effici} => {propos} 0.1000000 0.7500000 1.500000 3
## [2361] {addit,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [2362] {set,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [2363] {addit,
## work} => {set} 0.1000000 0.7500000 5.625000 3
## [2364] {addit,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2365] {dataset,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [2366] {dataset,
## addit} => {set} 0.1000000 0.7500000 5.625000 3
## [2367] {addit,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [2368] {propos,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [2369] {addit,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [2370] {network,
## set} => {addit} 0.1000000 1.0000000 6.000000 3
## [2371] {network,
## addit} => {set} 0.1000000 0.7500000 5.625000 3
## [2372] {process,
## set} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2373] {architectur,
## set} => {process} 0.1000000 1.0000000 5.000000 3
## [2374] {recognit,
## set} => {task} 0.1000000 1.0000000 2.727273 3
## [2375] {task,
## set} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2376] {task,
## recognit} => {set} 0.1000000 0.7500000 5.625000 3
## [2377] {recognit,
## set} => {data} 0.1000000 1.0000000 2.307692 3
## [2378] {data,
## set} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2379] {recognit,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [2380] {set,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2381] {task,
## set} => {data} 0.1000000 1.0000000 2.307692 3
## [2382] {data,
## set} => {task} 0.1000000 1.0000000 2.727273 3
## [2383] {task,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [2384] {set,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [2385] {set,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2386] {dataset,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [2387] {set,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [2388] {propos,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [2389] {set,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [2390] {network,
## set} => {work} 0.1000000 1.0000000 2.500000 3
## [2391] {data,
## set} => {learn} 0.1000000 1.0000000 2.307692 3
## [2392] {set,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [2393] {dataset,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [2394] {propos,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2395] {dataset,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [2396] {network,
## set} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2397] {propos,
## set} => {network} 0.1000000 1.0000000 1.578947 3
## [2398] {network,
## set} => {propos} 0.1000000 1.0000000 2.000000 3
## [2399] {shallow,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [2400] {featur,
## shallow} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [2401] {featur,
## techniqu} => {shallow} 0.1000000 0.7500000 4.500000 3
## [2402] {architectur,
## shallow} => {result} 0.1000000 1.0000000 3.000000 3
## [2403] {result,
## shallow} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2404] {architectur,
## result} => {shallow} 0.1000000 0.7500000 4.500000 3
## [2405] {architectur,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [2406] {neural,
## shallow} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2407] {architectur,
## neural} => {shallow} 0.1000000 0.7500000 4.500000 3
## [2408] {architectur,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2409] {featur,
## shallow} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2410] {architectur,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [2411] {network,
## shallow} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2412] {classif,
## shallow} => {method} 0.1000000 1.0000000 2.727273 3
## [2413] {method,
## shallow} => {classif} 0.1000000 1.0000000 3.750000 3
## [2414] {classif,
## shallow} => {approach} 0.1000000 1.0000000 2.500000 3
## [2415] {approach,
## shallow} => {classif} 0.1000000 1.0000000 3.750000 3
## [2416] {classif,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2417] {featur,
## shallow} => {classif} 0.1000000 0.7500000 2.812500 3
## [2418] {improv,
## shallow} => {perform} 0.1000000 1.0000000 2.142857 3
## [2419] {perform,
## shallow} => {improv} 0.1000000 1.0000000 3.333333 3
## [2420] {improv,
## shallow} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2421] {dataset,
## shallow} => {improv} 0.1000000 1.0000000 3.333333 3
## [2422] {improv,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2423] {featur,
## shallow} => {improv} 0.1000000 0.7500000 2.500000 3
## [2424] {featur,
## improv} => {shallow} 0.1000000 0.7500000 4.500000 3
## [2425] {result,
## shallow} => {neural} 0.1000000 1.0000000 3.000000 3
## [2426] {neural,
## shallow} => {result} 0.1000000 0.7500000 2.250000 3
## [2427] {result,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2428] {featur,
## shallow} => {result} 0.1000000 0.7500000 2.250000 3
## [2429] {result,
## shallow} => {network} 0.1000000 1.0000000 1.578947 3
## [2430] {network,
## shallow} => {result} 0.1000000 0.7500000 2.250000 3
## [2431] {neural,
## shallow} => {featur} 0.1000000 0.7500000 1.406250 3
## [2432] {featur,
## shallow} => {neural} 0.1000000 0.7500000 2.250000 3
## [2433] {neural,
## shallow} => {network} 0.1333333 1.0000000 1.578947 4
## [2434] {network,
## shallow} => {neural} 0.1333333 1.0000000 3.000000 4
## [2435] {method,
## shallow} => {approach} 0.1000000 1.0000000 2.500000 3
## [2436] {approach,
## shallow} => {method} 0.1000000 1.0000000 2.727273 3
## [2437] {method,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2438] {featur,
## shallow} => {method} 0.1000000 0.7500000 2.045455 3
## [2439] {approach,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2440] {featur,
## shallow} => {approach} 0.1000000 0.7500000 1.875000 3
## [2441] {perform,
## shallow} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2442] {dataset,
## shallow} => {perform} 0.1000000 1.0000000 2.142857 3
## [2443] {perform,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2444] {featur,
## shallow} => {perform} 0.1000000 0.7500000 1.607143 3
## [2445] {dataset,
## shallow} => {featur} 0.1000000 1.0000000 1.875000 3
## [2446] {featur,
## shallow} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2447] {featur,
## shallow} => {network} 0.1000000 0.7500000 1.184211 3
## [2448] {network,
## shallow} => {featur} 0.1000000 0.7500000 1.406250 3
## [2449] {semant,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [2450] {work,
## semant} => {larg} 0.1000000 0.7500000 4.500000 3
## [2451] {semant,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [2452] {represent,
## larg} => {semant} 0.1000000 0.7500000 4.500000 3
## [2453] {semant,
## larg} => {propos} 0.1000000 1.0000000 2.000000 3
## [2454] {propos,
## semant} => {larg} 0.1000000 0.7500000 4.500000 3
## [2455] {propos,
## larg} => {semant} 0.1000000 1.0000000 6.000000 3
## [2456] {task,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [2457] {approach,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [2458] {task,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2459] {work,
## semant} => {task} 0.1000000 0.7500000 2.045455 3
## [2460] {task,
## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [2461] {task,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [2462] {data,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [2463] {task,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2464] {dataset,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [2465] {task,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [2466] {learn,
## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [2467] {task,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2468] {task,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2469] {propos,
## semant} => {task} 0.1000000 0.7500000 2.045455 3
## [2470] {approach,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2471] {work,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [2472] {approach,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [2473] {data,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [2474] {approach,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2475] {dataset,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [2476] {approach,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [2477] {learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [2478] {approach,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2479] {approach,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2480] {propos,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [2481] {work,
## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [2482] {data,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2483] {work,
## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2484] {dataset,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2485] {work,
## semant} => {learn} 0.1000000 0.7500000 1.730769 3
## [2486] {learn,
## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [2487] {work,
## semant} => {represent} 0.1333333 1.0000000 2.000000 4
## [2488] {represent,
## semant} => {work} 0.1333333 0.8000000 2.000000 4
## [2489] {work,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [2490] {show,
## semant} => {work} 0.1000000 0.7500000 1.875000 3
## [2491] {work,
## semant} => {propos} 0.1333333 1.0000000 2.000000 4
## [2492] {propos,
## semant} => {work} 0.1333333 1.0000000 2.500000 4
## [2493] {data,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2494] {dataset,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [2495] {data,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [2496] {learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [2497] {data,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2498] {data,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2499] {propos,
## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [2500] {dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [2501] {learn,
## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2502] {dataset,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2503] {dataset,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2504] {propos,
## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2505] {learn,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2506] {learn,
## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [2507] {propos,
## semant} => {learn} 0.1000000 0.7500000 1.730769 3
## [2508] {represent,
## semant} => {show} 0.1333333 0.8000000 1.500000 4
## [2509] {show,
## semant} => {represent} 0.1333333 1.0000000 2.000000 4
## [2510] {represent,
## semant} => {propos} 0.1333333 0.8000000 1.600000 4
## [2511] {propos,
## semant} => {represent} 0.1333333 1.0000000 2.000000 4
## [2512] {model,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2513] {network,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [2514] {show,
## semant} => {propos} 0.1000000 0.7500000 1.500000 3
## [2515] {propos,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [2516] {show,
## semant} => {network} 0.1000000 0.7500000 1.184211 3
## [2517] {network,
## semant} => {show} 0.1000000 1.0000000 1.875000 3
## [2518] {process,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2519] {architectur,
## analysi} => {process} 0.1000000 0.7500000 3.750000 3
## [2520] {process,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [2521] {analysi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [2522] {process,
## work} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2523] {process,
## analysi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2524] {dataset,
## analysi} => {process} 0.1000000 1.0000000 5.000000 3
## [2525] {dataset,
## process} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2526] {process,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2527] {network,
## analysi} => {process} 0.1000000 0.7500000 3.750000 3
## [2528] {experi,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2529] {architectur,
## analysi} => {experi} 0.1000000 0.7500000 2.812500 3
## [2530] {architectur,
## experi} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2531] {experi,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [2532] {classif,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [2533] {classif,
## experi} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2534] {experi,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [2535] {propos,
## analysi} => {experi} 0.1000000 1.0000000 3.750000 3
## [2536] {experi,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2537] {network,
## analysi} => {experi} 0.1000000 0.7500000 2.812500 3
## [2538] {architectur,
## analysi} => {classif} 0.1000000 0.7500000 2.812500 3
## [2539] {classif,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2540] {classif,
## architectur} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2541] {architectur,
## analysi} => {neural} 0.1000000 0.7500000 2.250000 3
## [2542] {neural,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2543] {architectur,
## neural} => {analysi} 0.1000000 0.7500000 5.625000 3
## [2544] {architectur,
## analysi} => {train} 0.1000000 0.7500000 1.875000 3
## [2545] {train,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2546] {train,
## architectur} => {analysi} 0.1000000 1.0000000 7.500000 3
## [2547] {architectur,
## analysi} => {work} 0.1000000 0.7500000 1.875000 3
## [2548] {analysi,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2549] {architectur,
## analysi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2550] {dataset,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2551] {architectur,
## analysi} => {propos} 0.1000000 0.7500000 1.500000 3
## [2552] {propos,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2553] {architectur,
## analysi} => {featur} 0.1000000 0.7500000 1.406250 3
## [2554] {featur,
## analysi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [2555] {architectur,
## analysi} => {network} 0.1333333 1.0000000 1.578947 4
## [2556] {network,
## analysi} => {architectur} 0.1333333 1.0000000 3.750000 4
## [2557] {classif,
## analysi} => {propos} 0.1000000 1.0000000 2.000000 3
## [2558] {propos,
## analysi} => {classif} 0.1000000 1.0000000 3.750000 3
## [2559] {classif,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2560] {network,
## analysi} => {classif} 0.1000000 0.7500000 2.812500 3
## [2561] {neural,
## analysi} => {train} 0.1000000 1.0000000 2.500000 3
## [2562] {train,
## analysi} => {neural} 0.1000000 1.0000000 3.000000 3
## [2563] {neural,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2564] {network,
## analysi} => {neural} 0.1000000 0.7500000 2.250000 3
## [2565] {train,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2566] {network,
## analysi} => {train} 0.1000000 0.7500000 1.875000 3
## [2567] {analysi,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2568] {dataset,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [2569] {analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [2570] {network,
## analysi} => {work} 0.1000000 0.7500000 1.875000 3
## [2571] {dataset,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2572] {network,
## analysi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2573] {propos,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2574] {network,
## analysi} => {propos} 0.1000000 0.7500000 1.500000 3
## [2575] {featur,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [2576] {network,
## analysi} => {featur} 0.1000000 0.7500000 1.406250 3
## [2577] {appli,
## addit} => {method} 0.1000000 0.7500000 2.045455 3
## [2578] {method,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [2579] {method,
## appli} => {addit} 0.1000000 0.7500000 4.500000 3
## [2580] {appli,
## addit} => {work} 0.1000000 0.7500000 1.875000 3
## [2581] {addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [2582] {appli,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [2583] {appli,
## addit} => {perform} 0.1000000 0.7500000 1.607143 3
## [2584] {perform,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [2585] {appli,
## addit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2586] {dataset,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [2587] {appli,
## dataset} => {addit} 0.1000000 0.7500000 4.500000 3
## [2588] {appli,
## addit} => {show} 0.1000000 0.7500000 1.406250 3
## [2589] {show,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [2590] {show,
## appli} => {addit} 0.1000000 0.7500000 4.500000 3
## [2591] {appli,
## addit} => {propos} 0.1333333 1.0000000 2.000000 4
## [2592] {propos,
## addit} => {appli} 0.1333333 0.8000000 4.000000 4
## [2593] {appli,
## addit} => {network} 0.1000000 0.7500000 1.184211 3
## [2594] {network,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [2595] {network,
## appli} => {addit} 0.1000000 0.7500000 4.500000 3
## [2596] {addit,
## imag} => {classif} 0.1000000 1.0000000 3.750000 3
## [2597] {classif,
## addit} => {imag} 0.1000000 1.0000000 6.000000 3
## [2598] {classif,
## imag} => {addit} 0.1000000 1.0000000 6.000000 3
## [2599] {addit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [2600] {architectur,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [2601] {addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2602] {architectur,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2603] {dataset,
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## [2604] {architectur,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2605] {architectur,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [2606] {network,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [2607] {classif,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2608] {recognit,
## addit} => {learn} 0.1000000 1.0000000 2.307692 3
## [2609] {addit,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2610] {recognit,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2611] {method,
## addit} => {perform} 0.1000000 1.0000000 2.142857 3
## [2612] {perform,
## addit} => {method} 0.1000000 1.0000000 2.727273 3
## [2613] {method,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2614] {addit,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2615] {dataset,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [2616] {addit,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [2617] {propos,
## addit} => {work} 0.1333333 0.8000000 2.000000 4
## [2618] {addit,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [2619] {network,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [2620] {perform,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2621] {dataset,
## addit} => {propos} 0.1333333 1.0000000 2.000000 4
## [2622] {propos,
## addit} => {dataset} 0.1333333 0.8000000 1.846154 4
## [2623] {dataset,
## addit} => {network} 0.1333333 1.0000000 1.578947 4
## [2624] {network,
## addit} => {dataset} 0.1333333 1.0000000 2.307692 4
## [2625] {addit,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [2626] {show,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2627] {featur,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [2628] {propos,
## addit} => {network} 0.1333333 0.8000000 1.263158 4
## [2629] {network,
## addit} => {propos} 0.1333333 1.0000000 2.000000 4
## [2630] {solv,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [2631] {problem,
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## [2632] {problem,
## solv} => {function} 0.1000000 0.7500000 4.500000 3
## [2633] {recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [2634] {machin,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [2635] {recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [2636] {problem,
## function} => {recent} 0.1000000 0.7500000 3.214286 3
## [2637] {problem,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [2638] {recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [2639] {show,
## function} => {recent} 0.1000000 0.7500000 3.214286 3
## [2640] {recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [2641] {model,
## function} => {recent} 0.1000000 0.7500000 3.214286 3
## [2642] {machin,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [2643] {problem,
## function} => {machin} 0.1000000 0.7500000 3.214286 3
## [2644] {machin,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [2645] {machin,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [2646] {show,
## function} => {machin} 0.1000000 0.7500000 3.214286 3
## [2647] {machin,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [2648] {model,
## function} => {machin} 0.1000000 0.7500000 3.214286 3
## [2649] {optim,
## function} => {object} 0.1333333 1.0000000 3.750000 4
## [2650] {object,
## function} => {optim} 0.1333333 1.0000000 4.285714 4
## [2651] {object,
## optim} => {function} 0.1333333 1.0000000 6.000000 4
## [2652] {optim,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [2653] {problem,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [2654] {optim,
## function} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2655] {algorithm,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [2656] {optim,
## function} => {task} 0.1000000 0.7500000 2.045455 3
## [2657] {task,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [2658] {task,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [2659] {optim,
## function} => {data} 0.1000000 0.7500000 1.730769 3
## [2660] {data,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [2661] {data,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2662] {optim,
## function} => {show} 0.1000000 0.7500000 1.406250 3
## [2663] {show,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [2664] {show,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2665] {optim,
## function} => {propos} 0.1000000 0.7500000 1.500000 3
## [2666] {propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [2667] {propos,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2668] {optim,
## function} => {model} 0.1000000 0.7500000 1.406250 3
## [2669] {model,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [2670] {model,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2671] {optim,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [2672] {featur,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [2673] {featur,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [2674] {object,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [2675] {problem,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [2676] {object,
## problem} => {function} 0.1000000 0.7500000 4.500000 3
## [2677] {object,
## function} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2678] {algorithm,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [2679] {algorithm,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [2680] {object,
## function} => {task} 0.1000000 0.7500000 2.045455 3
## [2681] {task,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [2682] {task,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [2683] {object,
## function} => {data} 0.1000000 0.7500000 1.730769 3
## [2684] {data,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [2685] {data,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [2686] {object,
## function} => {show} 0.1000000 0.7500000 1.406250 3
## [2687] {show,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [2688] {object,
## function} => {propos} 0.1000000 0.7500000 1.500000 3
## [2689] {propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [2690] {object,
## function} => {model} 0.1000000 0.7500000 1.406250 3
## [2691] {model,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [2692] {object,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [2693] {featur,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [2694] {problem,
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## [2747] {accuraci,
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## [2748] {achiev,
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## [2751] {achiev,
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## [2761] {accuraci,
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## [2762] {achiev,
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## [2764] {represent,
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## [2765] {represent,
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## [2766] {accuraci,
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## [2767] {accuraci,
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## [2768] {accuraci,
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## [2769] {featur,
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## [2770] {featur,
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## [2771] {accuraci,
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## [2772] {network,
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## [2773] {classif,
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## [2774] {method,
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## [2775] {classif,
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## [2776] {accuraci,
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## [2787] {perform,
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## [2788] {accuraci,
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## [2789] {accuraci,
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## [2790] {accuraci,
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## [2791] {accuraci,
## learn} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2792] {accuraci,
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## [2793] {represent,
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## [2794] {accuraci,
## recognit} => {show} 0.1000000 0.7500000 1.406250 3
## [2795] {show,
## accuraci} => {recognit} 0.1000000 1.0000000 3.333333 3
## [2796] {accuraci,
## recognit} => {propos} 0.1000000 0.7500000 1.500000 3
## [2797] {accuraci,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [2798] {accuraci,
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## [2799] {featur,
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## [2800] {accuraci,
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## [2801] {accuraci,
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## [2802] {network,
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## [2803] {accuraci,
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## [2804] {accuraci,
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## [2805] {accuraci,
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## [2806] {method,
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## [2807] {accuraci,
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## [2810] {accuraci,
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## [2811] {train,
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## [2813] {train,
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## [2814] {accuraci,
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## [2815] {accuraci,
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## [2816] {accuraci,
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## [2817] {accuraci,
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## [2818] {accuraci,
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## [2819] {represent,
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## [2820] {accuraci,
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## [2821] {accuraci,
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## [2822] {accuraci,
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## [2823] {featur,
## accuraci} => {learn} 0.1333333 0.8000000 1.846154 4
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## [2827] {featur,
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## [2828] {show,
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## [2829] {accuraci,
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## [2830] {featur,
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## [2833] {algorithm,
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## [2834] {algorithm,
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## [2837] {neural,
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## [2838] {approach,
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## [2841] {neural,
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## [2842] {network,
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## [2843] {train,
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## [2844] {approach,
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## [2845] {train,
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## [2846] {propos,
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## [2848] {network,
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## [2849] {approach,
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## [2850] {propos,
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## [2851] {approach,
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## [2853] {propos,
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## [2858] {specif,
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## [2859] {neural,
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## [2860] {neural,
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## [2861] {specif,
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## [2862] {train,
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## [2863] {specif,
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## [2864] {dataset,
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## [2865] {dataset,
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## [2866] {specif,
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## [2867] {network,
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## [2868] {network,
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## [2869] {layer,
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## [2870] {applic,
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## [2871] {layer,
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## [2873] {result,
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## [2874] {layer,
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## [2875] {data,
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## [2876] {data,
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## [2879] {result,
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## [2881] {data,
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## [2882] {result,
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## [2883] {neural,
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## [2884] {result,
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## [2886] {result,
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## [2888] {result,
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## [2889] {data,
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## [2890] {result,
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## [2891] {dataset,
## potenti} => {result} 0.1000000 0.7500000 2.250000 3
## [2892] {result,
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## [2893] {network,
## potenti} => {result} 0.1000000 0.7500000 2.250000 3
## [2894] {neural,
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## [2895] {train,
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## [2896] {neural,
## potenti} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2897] {dataset,
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## [2898] {neural,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [2899] {network,
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## [2900] {train,
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## [2901] {dataset,
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## [2902] {train,
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## [2903] {network,
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## [2904] {work,
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## [2906] {work,
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## [2907] {dataset,
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## [2908] {work,
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## [2909] {network,
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## [2910] {data,
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## [2911] {dataset,
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## [2912] {data,
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## [2913] {network,
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## [2914] {dataset,
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## [2915] {propos,
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## [2916] {dataset,
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## [2917] {network,
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## [2918] {propos,
## potenti} => {network} 0.1000000 1.0000000 1.578947 3
## [2919] {network,
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## [2920] {solv,
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## [2921] {problem,
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## [2922] {problem,
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## [2923] {solv,
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## [2924] {model,
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## [2925] {model,
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## [2926] {make,
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## [2927] {problem,
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## [2928] {make,
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## [2929] {approach,
## solv} => {make} 0.1000000 1.0000000 3.333333 3
## [2930] {make,
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## [2931] {perform,
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## [2932] {make,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2933] {featur,
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## [2934] {problem,
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## [2935] {approach,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [2936] {problem,
## solv} => {perform} 0.1000000 0.7500000 1.607143 3
## [2937] {perform,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [2938] {problem,
## solv} => {model} 0.1000000 0.7500000 1.406250 3
## [2939] {model,
## solv} => {problem} 0.1000000 1.0000000 3.333333 3
## [2940] {problem,
## solv} => {featur} 0.1000000 0.7500000 1.406250 3
## [2941] {featur,
## solv} => {problem} 0.1000000 0.7500000 2.500000 3
## [2942] {task,
## solv} => {data} 0.1333333 1.0000000 2.307692 4
## [2943] {data,
## solv} => {task} 0.1333333 1.0000000 2.727273 4
## [2944] {task,
## solv} => {represent} 0.1000000 0.7500000 1.500000 3
## [2945] {represent,
## solv} => {task} 0.1000000 1.0000000 2.727273 3
## [2946] {task,
## solv} => {featur} 0.1000000 0.7500000 1.406250 3
## [2947] {featur,
## solv} => {task} 0.1000000 0.7500000 2.045455 3
## [2948] {approach,
## solv} => {perform} 0.1000000 1.0000000 2.142857 3
## [2949] {perform,
## solv} => {approach} 0.1000000 1.0000000 2.500000 3
## [2950] {approach,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2951] {featur,
## solv} => {approach} 0.1000000 0.7500000 1.875000 3
## [2952] {perform,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2953] {featur,
## solv} => {perform} 0.1000000 0.7500000 1.607143 3
## [2954] {data,
## solv} => {represent} 0.1000000 0.7500000 1.500000 3
## [2955] {represent,
## solv} => {data} 0.1000000 1.0000000 2.307692 3
## [2956] {data,
## solv} => {featur} 0.1000000 0.7500000 1.406250 3
## [2957] {featur,
## solv} => {data} 0.1000000 0.7500000 1.730769 3
## [2958] {dataset,
## solv} => {learn} 0.1000000 1.0000000 2.307692 3
## [2959] {learn,
## solv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2960] {dataset,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2961] {featur,
## solv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2962] {learn,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2963] {featur,
## solv} => {learn} 0.1000000 0.7500000 1.730769 3
## [2964] {represent,
## solv} => {featur} 0.1000000 1.0000000 1.875000 3
## [2965] {featur,
## solv} => {represent} 0.1000000 0.7500000 1.500000 3
## [2966] {layer,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [2967] {result,
## specif} => {layer} 0.1000000 0.7500000 3.750000 3
## [2968] {layer,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [2969] {train,
## layer} => {specif} 0.1000000 1.0000000 6.000000 3
## [2970] {layer,
## specif} => {work} 0.1000000 1.0000000 2.500000 3
## [2971] {work,
## specif} => {layer} 0.1000000 1.0000000 5.000000 3
## [2972] {work,
## layer} => {specif} 0.1000000 0.7500000 4.500000 3
## [2973] {layer,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [2974] {network,
## specif} => {layer} 0.1000000 0.7500000 3.750000 3
## [2975] {network,
## layer} => {specif} 0.1000000 0.7500000 4.500000 3
## [2976] {result,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [2977] {neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [2978] {result,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2979] {algorithm,
## specif} => {result} 0.1000000 0.7500000 2.250000 3
## [2980] {result,
## specif} => {train} 0.1333333 1.0000000 2.500000 4
## [2981] {train,
## specif} => {result} 0.1333333 0.8000000 2.400000 4
## [2982] {result,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [2983] {work,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [2984] {result,
## work} => {specif} 0.1000000 0.7500000 4.500000 3
## [2985] {result,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [2986] {dataset,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [2987] {result,
## specif} => {network} 0.1333333 1.0000000 1.578947 4
## [2988] {network,
## specif} => {result} 0.1333333 1.0000000 3.000000 4
## [2989] {neural,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [2990] {neural,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [2991] {dataset,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [2992] {neural,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [2993] {network,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [2994] {algorithm,
## specif} => {train} 0.1333333 1.0000000 2.500000 4
## [2995] {train,
## specif} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [2996] {algorithm,
## specif} => {network} 0.1000000 0.7500000 1.184211 3
## [2997] {network,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [2998] {work,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [2999] {data,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [3000] {dataset,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [3001] {propos,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [3002] {train,
## specif} => {network} 0.1333333 0.8000000 1.263158 4
## [3003] {network,
## specif} => {train} 0.1333333 1.0000000 2.500000 4
## [3004] {work,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [3005] {network,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [3006] {dataset,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [3007] {network,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3008] {layer,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3009] {result,
## techniqu} => {layer} 0.1000000 0.7500000 3.750000 3
## [3010] {layer,
## techniqu} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3011] {algorithm,
## techniqu} => {layer} 0.1000000 1.0000000 5.000000 3
## [3012] {machin,
## techniqu} => {learn} 0.1000000 1.0000000 2.307692 3
## [3013] {learn,
## techniqu} => {machin} 0.1000000 1.0000000 4.285714 3
## [3014] {machin,
## learn} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [3015] {machin,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [3016] {featur,
## techniqu} => {machin} 0.1000000 0.7500000 3.214286 3
## [3017] {success,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3018] {result,
## techniqu} => {success} 0.1000000 0.7500000 2.812500 3
## [3019] {result,
## success} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [3020] {architectur,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3021] {result,
## techniqu} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3022] {architectur,
## result} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [3023] {architectur,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [3024] {represent,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3025] {represent,
## architectur} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [3026] {architectur,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [3027] {featur,
## techniqu} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3028] {result,
## techniqu} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3029] {algorithm,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3030] {result,
## techniqu} => {train} 0.1000000 0.7500000 1.875000 3
## [3031] {train,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3032] {result,
## techniqu} => {represent} 0.1000000 0.7500000 1.500000 3
## [3033] {represent,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3034] {result,
## techniqu} => {featur} 0.1000000 0.7500000 1.406250 3
## [3035] {featur,
## techniqu} => {result} 0.1000000 0.7500000 2.250000 3
## [3036] {result,
## techniqu} => {network} 0.1000000 0.7500000 1.184211 3
## [3037] {network,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [3038] {train,
## techniqu} => {network} 0.1000000 1.0000000 1.578947 3
## [3039] {network,
## techniqu} => {train} 0.1000000 1.0000000 2.500000 3
## [3040] {learn,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [3041] {featur,
## techniqu} => {learn} 0.1000000 0.7500000 1.730769 3
## [3042] {represent,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [3043] {featur,
## techniqu} => {represent} 0.1000000 0.7500000 1.500000 3
## [3044] {appli,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [3045] {represent,
## appli} => {applic} 0.1000000 1.0000000 4.285714 3
## [3046] {appli,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [3047] {appli,
## object} => {perform} 0.1000000 1.0000000 2.142857 3
## [3048] {appli,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [3049] {appli,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [3050] {featur,
## appli} => {object} 0.1000000 0.7500000 2.812500 3
## [3051] {appli,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [3052] {method,
## appli} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3053] {method,
## architectur} => {appli} 0.1000000 0.7500000 3.750000 3
## [3054] {appli,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [3055] {appli,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3056] {appli,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3057] {appli,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [3058] {appli,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [3059] {network,
## appli} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3060] {classif,
## appli} => {method} 0.1000000 1.0000000 2.727273 3
## [3061] {method,
## appli} => {classif} 0.1000000 0.7500000 2.812500 3
## [3062] {classif,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [3063] {classif,
## appli} => {show} 0.1000000 1.0000000 1.875000 3
## [3064] {show,
## appli} => {classif} 0.1000000 0.7500000 2.812500 3
## [3065] {classif,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [3066] {appli,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [3067] {appli,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [3068] {appli,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [3069] {show,
## appli} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3070] {appli,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [3071] {appli,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [3072] {approach,
## appli} => {neural} 0.1000000 0.7500000 2.250000 3
## [3073] {appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3074] {appli,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [3075] {appli,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [3076] {show,
## appli} => {neural} 0.1000000 0.7500000 2.250000 3
## [3077] {appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [3078] {appli,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [3079] {network,
## appli} => {neural} 0.1000000 0.7500000 2.250000 3
## [3080] {method,
## appli} => {perform} 0.1333333 1.0000000 2.142857 4
## [3081] {appli,
## perform} => {method} 0.1333333 0.8000000 2.181818 4
## [3082] {method,
## appli} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3083] {appli,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [3084] {method,
## appli} => {show} 0.1000000 0.7500000 1.406250 3
## [3085] {show,
## appli} => {method} 0.1000000 0.7500000 2.045455 3
## [3086] {method,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3087] {method,
## appli} => {featur} 0.1000000 0.7500000 1.406250 3
## [3088] {featur,
## appli} => {method} 0.1000000 0.7500000 2.045455 3
## [3089] {method,
## appli} => {network} 0.1000000 0.7500000 1.184211 3
## [3090] {network,
## appli} => {method} 0.1000000 0.7500000 2.045455 3
## [3091] {algorithm,
## appli} => {approach} 0.1000000 1.0000000 2.500000 3
## [3092] {approach,
## appli} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3093] {algorithm,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [3094] {algorithm,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [3095] {approach,
## appli} => {perform} 0.1000000 0.7500000 1.607143 3
## [3096] {approach,
## appli} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3097] {appli,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [3098] {approach,
## appli} => {show} 0.1000000 0.7500000 1.406250 3
## [3099] {show,
## appli} => {approach} 0.1000000 0.7500000 1.875000 3
## [3100] {approach,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3101] {approach,
## appli} => {network} 0.1000000 0.7500000 1.184211 3
## [3102] {network,
## appli} => {approach} 0.1000000 0.7500000 1.875000 3
## [3103] {appli,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3104] {appli,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [3105] {appli,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [3106] {appli,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [3107] {network,
## appli} => {work} 0.1000000 0.7500000 1.875000 3
## [3108] {appli,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [3109] {show,
## appli} => {perform} 0.1000000 0.7500000 1.607143 3
## [3110] {appli,
## perform} => {propos} 0.1666667 1.0000000 2.000000 5
## [3111] {appli,
## propos} => {perform} 0.1666667 0.8333333 1.785714 5
## [3112] {appli,
## perform} => {featur} 0.1333333 0.8000000 1.500000 4
## [3113] {featur,
## appli} => {perform} 0.1333333 1.0000000 2.142857 4
## [3114] {network,
## appli} => {perform} 0.1000000 0.7500000 1.607143 3
## [3115] {appli,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [3116] {show,
## appli} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3117] {appli,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [3118] {appli,
## dataset} => {network} 0.1333333 1.0000000 1.578947 4
## [3119] {network,
## appli} => {dataset} 0.1333333 1.0000000 2.307692 4
## [3120] {represent,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [3121] {show,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3122] {show,
## appli} => {network} 0.1000000 0.7500000 1.184211 3
## [3123] {network,
## appli} => {show} 0.1000000 0.7500000 1.406250 3
## [3124] {featur,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3125] {network,
## appli} => {propos} 0.1333333 1.0000000 2.000000 4
## [3126] {perform,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3127] {data,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3128] {dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3129] {work,
## larg} => {represent} 0.1333333 0.8000000 1.600000 4
## [3130] {represent,
## larg} => {work} 0.1333333 1.0000000 2.500000 4
## [3131] {propos,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3132] {featur,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3133] {network,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [3134] {perform,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3135] {represent,
## larg} => {perform} 0.1000000 0.7500000 1.607143 3
## [3136] {data,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3137] {dataset,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [3138] {data,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3139] {represent,
## larg} => {data} 0.1000000 0.7500000 1.730769 3
## [3140] {data,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [3141] {featur,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [3142] {dataset,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3143] {represent,
## larg} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3144] {dataset,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [3145] {featur,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3146] {represent,
## larg} => {propos} 0.1000000 0.7500000 1.500000 3
## [3147] {propos,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3148] {represent,
## larg} => {featur} 0.1000000 0.7500000 1.406250 3
## [3149] {featur,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [3150] {achiev,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3151] {data,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3152] {data,
## achiev} => {challeng} 0.1000000 0.7500000 4.500000 3
## [3153] {achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3154] {dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [3155] {achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3156] {learn,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3157] {achiev,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [3158] {achiev,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [3159] {represent,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3160] {represent,
## achiev} => {challeng} 0.1000000 0.7500000 4.500000 3
## [3161] {achiev,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3162] {featur,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [3163] {signific,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [3164] {train,
## challeng} => {signific} 0.1000000 0.7500000 2.812500 3
## [3165] {signific,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [3166] {show,
## challeng} => {signific} 0.1000000 0.7500000 2.812500 3
## [3167] {object,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [3168] {show,
## challeng} => {object} 0.1000000 0.7500000 2.812500 3
## [3169] {object,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [3170] {propos,
## challeng} => {object} 0.1000000 0.7500000 2.812500 3
## [3171] {paper,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [3172] {train,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [3173] {paper,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3174] {data,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [3175] {paper,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3176] {learn,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [3177] {paper,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3178] {featur,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [3179] {recognit,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [3180] {train,
## challeng} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3181] {train,
## recognit} => {challeng} 0.1000000 0.7500000 4.500000 3
## [3182] {recognit,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [3183] {represent,
## challeng} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3184] {task,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3185] {data,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [3186] {task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3187] {learn,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [3188] {task,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [3189] {show,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [3190] {task,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3191] {featur,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [3192] {train,
## challeng} => {perform} 0.1000000 0.7500000 1.607143 3
## [3193] {perform,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [3194] {train,
## challeng} => {data} 0.1000000 0.7500000 1.730769 3
## [3195] {data,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3196] {train,
## challeng} => {learn} 0.1000000 0.7500000 1.730769 3
## [3197] {learn,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3198] {train,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3199] {represent,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3200] {train,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3201] {show,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3202] {train,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3203] {propos,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3204] {train,
## challeng} => {featur} 0.1000000 0.7500000 1.406250 3
## [3205] {featur,
## challeng} => {train} 0.1000000 0.7500000 1.875000 3
## [3206] {perform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [3207] {propos,
## challeng} => {perform} 0.1000000 0.7500000 1.607143 3
## [3208] {data,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3209] {dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3210] {data,
## challeng} => {learn} 0.1333333 1.0000000 2.307692 4
## [3211] {learn,
## challeng} => {data} 0.1333333 1.0000000 2.307692 4
## [3212] {data,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3213] {represent,
## challeng} => {data} 0.1000000 0.7500000 1.730769 3
## [3214] {data,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3215] {show,
## challeng} => {data} 0.1000000 0.7500000 1.730769 3
## [3216] {data,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3217] {propos,
## challeng} => {data} 0.1000000 0.7500000 1.730769 3
## [3218] {data,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [3219] {model,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [3220] {data,
## challeng} => {featur} 0.1333333 1.0000000 1.875000 4
## [3221] {featur,
## challeng} => {data} 0.1333333 1.0000000 2.307692 4
## [3222] {dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3223] {learn,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3224] {dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [3225] {represent,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3226] {dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3227] {featur,
## challeng} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3228] {learn,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3229] {represent,
## challeng} => {learn} 0.1000000 0.7500000 1.730769 3
## [3230] {learn,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3231] {show,
## challeng} => {learn} 0.1000000 0.7500000 1.730769 3
## [3232] {learn,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3233] {propos,
## challeng} => {learn} 0.1000000 0.7500000 1.730769 3
## [3234] {learn,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [3235] {model,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [3236] {learn,
## challeng} => {featur} 0.1333333 1.0000000 1.875000 4
## [3237] {featur,
## challeng} => {learn} 0.1333333 1.0000000 2.307692 4
## [3238] {represent,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3239] {show,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3240] {represent,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3241] {propos,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3242] {represent,
## challeng} => {featur} 0.1000000 0.7500000 1.406250 3
## [3243] {featur,
## challeng} => {represent} 0.1000000 0.7500000 1.500000 3
## [3244] {show,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3245] {propos,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3246] {show,
## challeng} => {featur} 0.1000000 0.7500000 1.406250 3
## [3247] {featur,
## challeng} => {show} 0.1000000 0.7500000 1.406250 3
## [3248] {propos,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [3249] {model,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [3250] {propos,
## challeng} => {featur} 0.1000000 0.7500000 1.406250 3
## [3251] {featur,
## challeng} => {propos} 0.1000000 0.7500000 1.500000 3
## [3252] {model,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [3253] {featur,
## challeng} => {model} 0.1000000 0.7500000 1.406250 3
## [3254] {demonstr,
## imag} => {work} 0.1000000 1.0000000 2.500000 3
## [3255] {imag,
## work} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [3256] {demonstr,
## work} => {imag} 0.1000000 0.7500000 4.500000 3
## [3257] {demonstr,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3258] {propos,
## demonstr} => {imag} 0.1000000 0.7500000 4.500000 3
## [3259] {object,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3260] {recognit,
## imag} => {object} 0.1000000 0.7500000 2.812500 3
## [3261] {object,
## recognit} => {imag} 0.1000000 0.7500000 4.500000 3
## [3262] {object,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3263] {classif,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3264] {recognit,
## imag} => {perform} 0.1000000 0.7500000 1.607143 3
## [3265] {perform,
## imag} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3266] {perform,
## recognit} => {imag} 0.1000000 0.7500000 4.500000 3
## [3267] {recognit,
## imag} => {learn} 0.1000000 0.7500000 1.730769 3
## [3268] {imag,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3269] {recognit,
## imag} => {represent} 0.1000000 0.7500000 1.500000 3
## [3270] {represent,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3271] {recognit,
## imag} => {propos} 0.1333333 1.0000000 2.000000 4
## [3272] {propos,
## imag} => {recognit} 0.1333333 0.8000000 2.666667 4
## [3273] {recognit,
## imag} => {featur} 0.1000000 0.7500000 1.406250 3
## [3274] {featur,
## imag} => {recognit} 0.1000000 1.0000000 3.333333 3
## [3275] {improv,
## imag} => {train} 0.1000000 1.0000000 2.500000 3
## [3276] {train,
## imag} => {improv} 0.1000000 1.0000000 3.333333 3
## [3277] {improv,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [3278] {perform,
## imag} => {improv} 0.1000000 0.7500000 2.500000 3
## [3279] {improv,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3280] {train,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [3281] {perform,
## imag} => {train} 0.1000000 0.7500000 1.875000 3
## [3282] {train,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3283] {imag,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [3284] {perform,
## imag} => {represent} 0.1000000 0.7500000 1.500000 3
## [3285] {represent,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [3286] {perform,
## imag} => {show} 0.1000000 0.7500000 1.406250 3
## [3287] {show,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [3288] {perform,
## imag} => {propos} 0.1333333 1.0000000 2.000000 4
## [3289] {propos,
## imag} => {perform} 0.1333333 0.8000000 1.714286 4
## [3290] {dataset,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3291] {imag,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [3292] {imag,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [3293] {featur,
## imag} => {learn} 0.1000000 1.0000000 2.307692 3
## [3294] {represent,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3295] {show,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3296] {featur,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [3297] {framework,
## requir} => {train} 0.1000000 1.0000000 2.500000 3
## [3298] {train,
## framework} => {requir} 0.1000000 0.7500000 3.750000 3
## [3299] {train,
## requir} => {framework} 0.1000000 0.7500000 3.750000 3
## [3300] {comput,
## framework} => {reduc} 0.1000000 1.0000000 4.285714 3
## [3301] {reduc,
## framework} => {comput} 0.1000000 1.0000000 4.285714 3
## [3302] {reduc,
## comput} => {framework} 0.1000000 0.7500000 3.750000 3
## [3303] {train,
## framework} => {work} 0.1000000 0.7500000 1.875000 3
## [3304] {work,
## framework} => {train} 0.1000000 0.7500000 1.875000 3
## [3305] {train,
## framework} => {show} 0.1000000 0.7500000 1.406250 3
## [3306] {show,
## framework} => {train} 0.1000000 1.0000000 2.500000 3
## [3307] {work,
## framework} => {network} 0.1000000 0.7500000 1.184211 3
## [3308] {network,
## framework} => {work} 0.1000000 0.7500000 1.875000 3
## [3309] {demonstr,
## requir} => {perform} 0.1000000 1.0000000 2.142857 3
## [3310] {perform,
## requir} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [3311] {perform,
## demonstr} => {requir} 0.1000000 0.7500000 3.750000 3
## [3312] {problem,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [3313] {model,
## requir} => {problem} 0.1000000 0.7500000 2.500000 3
## [3314] {improv,
## requir} => {train} 0.1000000 0.7500000 1.875000 3
## [3315] {train,
## requir} => {improv} 0.1000000 0.7500000 2.500000 3
## [3316] {improv,
## requir} => {show} 0.1000000 0.7500000 1.406250 3
## [3317] {show,
## requir} => {improv} 0.1000000 1.0000000 3.333333 3
## [3318] {improv,
## requir} => {model} 0.1000000 0.7500000 1.406250 3
## [3319] {model,
## requir} => {improv} 0.1000000 0.7500000 2.500000 3
## [3320] {algorithm,
## requir} => {train} 0.1000000 1.0000000 2.500000 3
## [3321] {train,
## requir} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3322] {approach,
## requir} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3323] {dataset,
## requir} => {approach} 0.1000000 1.0000000 2.500000 3
## [3324] {approach,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [3325] {model,
## requir} => {approach} 0.1000000 0.7500000 1.875000 3
## [3326] {dataset,
## requir} => {model} 0.1000000 1.0000000 1.875000 3
## [3327] {model,
## requir} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3328] {reduc,
## exist} => {optim} 0.1000000 1.0000000 4.285714 3
## [3329] {exist,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [3330] {reduc,
## optim} => {exist} 0.1000000 0.7500000 4.500000 3
## [3331] {reduc,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [3332] {exist,
## work} => {reduc} 0.1000000 0.7500000 3.214286 3
## [3333] {reduc,
## work} => {exist} 0.1000000 1.0000000 6.000000 3
## [3334] {reduc,
## exist} => {network} 0.1000000 1.0000000 1.578947 3
## [3335] {network,
## exist} => {reduc} 0.1000000 0.7500000 3.214286 3
## [3336] {exist,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [3337] {exist,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [3338] {optim,
## work} => {exist} 0.1000000 0.7500000 4.500000 3
## [3339] {exist,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [3340] {network,
## exist} => {optim} 0.1000000 0.7500000 3.214286 3
## [3341] {network,
## optim} => {exist} 0.1000000 0.7500000 4.500000 3
## [3342] {improv,
## exist} => {perform} 0.1000000 1.0000000 2.142857 3
## [3343] {perform,
## exist} => {improv} 0.1000000 1.0000000 3.333333 3
## [3344] {approach,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [3345] {exist,
## work} => {approach} 0.1000000 0.7500000 1.875000 3
## [3346] {approach,
## exist} => {show} 0.1000000 1.0000000 1.875000 3
## [3347] {show,
## exist} => {approach} 0.1000000 1.0000000 2.500000 3
## [3348] {approach,
## exist} => {network} 0.1000000 1.0000000 1.578947 3
## [3349] {network,
## exist} => {approach} 0.1000000 0.7500000 1.875000 3
## [3350] {exist,
## work} => {show} 0.1000000 0.7500000 1.406250 3
## [3351] {show,
## exist} => {work} 0.1000000 1.0000000 2.500000 3
## [3352] {exist,
## work} => {model} 0.1000000 0.7500000 1.406250 3
## [3353] {model,
## exist} => {work} 0.1000000 0.7500000 1.875000 3
## [3354] {exist,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [3355] {network,
## exist} => {work} 0.1333333 1.0000000 2.500000 4
## [3356] {dataset,
## exist} => {propos} 0.1000000 1.0000000 2.000000 3
## [3357] {propos,
## exist} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3358] {exist,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [3359] {represent,
## exist} => {learn} 0.1000000 1.0000000 2.307692 3
## [3360] {exist,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [3361] {model,
## exist} => {learn} 0.1000000 0.7500000 1.730769 3
## [3362] {exist,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [3363] {featur,
## exist} => {learn} 0.1000000 1.0000000 2.307692 3
## [3364] {represent,
## exist} => {model} 0.1000000 1.0000000 1.875000 3
## [3365] {model,
## exist} => {represent} 0.1000000 0.7500000 1.500000 3
## [3366] {represent,
## exist} => {featur} 0.1000000 1.0000000 1.875000 3
## [3367] {featur,
## exist} => {represent} 0.1000000 1.0000000 2.000000 3
## [3368] {show,
## exist} => {network} 0.1000000 1.0000000 1.578947 3
## [3369] {network,
## exist} => {show} 0.1000000 0.7500000 1.406250 3
## [3370] {model,
## exist} => {featur} 0.1000000 0.7500000 1.406250 3
## [3371] {featur,
## exist} => {model} 0.1000000 1.0000000 1.875000 3
## [3372] {model,
## exist} => {network} 0.1000000 0.7500000 1.184211 3
## [3373] {network,
## exist} => {model} 0.1000000 0.7500000 1.406250 3
## [3374] {layer,
## applic} => {result} 0.1000000 1.0000000 3.000000 3
## [3375] {result,
## applic} => {layer} 0.1000000 0.7500000 3.750000 3
## [3376] {layer,
## applic} => {data} 0.1000000 1.0000000 2.307692 3
## [3377] {data,
## layer} => {applic} 0.1000000 0.7500000 3.214286 3
## [3378] {process,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3379] {process,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [3380] {represent,
## layer} => {process} 0.1000000 0.7500000 3.750000 3
## [3381] {represent,
## process} => {layer} 0.1000000 1.0000000 5.000000 3
## [3382] {process,
## layer} => {featur} 0.1000000 1.0000000 1.875000 3
## [3383] {featur,
## layer} => {process} 0.1000000 1.0000000 5.000000 3
## [3384] {result,
## layer} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [3385] {algorithm,
## layer} => {result} 0.1333333 0.8000000 2.400000 4
## [3386] {train,
## layer} => {result} 0.1000000 1.0000000 3.000000 3
## [3387] {work,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [3388] {result,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [3389] {result,
## layer} => {data} 0.1333333 0.8000000 1.846154 4
## [3390] {data,
## layer} => {result} 0.1333333 1.0000000 3.000000 4
## [3391] {represent,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [3392] {show,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [3393] {network,
## layer} => {result} 0.1000000 0.7500000 2.250000 3
## [3394] {neural,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [3395] {work,
## layer} => {neural} 0.1000000 0.7500000 2.250000 3
## [3396] {neural,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [3397] {represent,
## layer} => {neural} 0.1000000 0.7500000 2.250000 3
## [3398] {neural,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [3399] {network,
## layer} => {neural} 0.1000000 0.7500000 2.250000 3
## [3400] {work,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3401] {algorithm,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [3402] {data,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3403] {represent,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3404] {represent,
## algorithm} => {layer} 0.1000000 0.7500000 3.750000 3
## [3405] {show,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3406] {model,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3407] {featur,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3408] {network,
## layer} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3409] {train,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [3410] {work,
## layer} => {train} 0.1000000 0.7500000 1.875000 3
## [3411] {train,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [3412] {network,
## layer} => {train} 0.1000000 0.7500000 1.875000 3
## [3413] {work,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [3414] {represent,
## layer} => {work} 0.1000000 0.7500000 1.875000 3
## [3415] {work,
## layer} => {network} 0.1333333 1.0000000 1.578947 4
## [3416] {network,
## layer} => {work} 0.1333333 1.0000000 2.500000 4
## [3417] {data,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [3418] {represent,
## layer} => {data} 0.1000000 0.7500000 1.730769 3
## [3419] {data,
## layer} => {show} 0.1000000 0.7500000 1.406250 3
## [3420] {show,
## layer} => {data} 0.1000000 0.7500000 1.730769 3
## [3421] {layer,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [3422] {represent,
## layer} => {learn} 0.1000000 0.7500000 1.730769 3
## [3423] {layer,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [3424] {show,
## layer} => {learn} 0.1000000 0.7500000 1.730769 3
## [3425] {represent,
## layer} => {show} 0.1000000 0.7500000 1.406250 3
## [3426] {show,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [3427] {represent,
## layer} => {featur} 0.1000000 0.7500000 1.406250 3
## [3428] {featur,
## layer} => {represent} 0.1000000 1.0000000 2.000000 3
## [3429] {represent,
## layer} => {network} 0.1000000 0.7500000 1.184211 3
## [3430] {network,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [3431] {show,
## layer} => {model} 0.1000000 0.7500000 1.406250 3
## [3432] {model,
## layer} => {show} 0.1000000 1.0000000 1.875000 3
## [3433] {complex,
## input} => {approach} 0.1000000 1.0000000 2.500000 3
## [3434] {approach,
## complex} => {input} 0.1000000 1.0000000 4.285714 3
## [3435] {complex,
## input} => {show} 0.1000000 1.0000000 1.875000 3
## [3436] {complex,
## input} => {model} 0.1000000 1.0000000 1.875000 3
## [3437] {complex,
## model} => {input} 0.1000000 1.0000000 4.285714 3
## [3438] {complex,
## input} => {featur} 0.1000000 1.0000000 1.875000 3
## [3439] {featur,
## input} => {complex} 0.1000000 0.7500000 3.750000 3
## [3440] {classif,
## complex} => {method} 0.1000000 1.0000000 2.727273 3
## [3441] {complex,
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## applic} => {learn} 0.1000000 0.7500000 1.730769 3
## [3695] {learn,
## applic} => {propos} 0.1333333 1.0000000 2.000000 4
## [3696] {propos,
## applic} => {learn} 0.1333333 0.8000000 1.846154 4
## [3697] {show,
## applic} => {represent} 0.1000000 0.7500000 1.500000 3
## [3698] {represent,
## applic} => {propos} 0.1666667 0.8333333 1.666667 5
## [3699] {propos,
## applic} => {represent} 0.1666667 1.0000000 2.000000 5
## [3700] {featur,
## applic} => {represent} 0.1333333 1.0000000 2.000000 4
## [3701] {show,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [3702] {featur,
## applic} => {propos} 0.1000000 0.7500000 1.500000 3
## [3703] {input,
## recent} => {approach} 0.1000000 1.0000000 2.500000 3
## [3704] {approach,
## recent} => {input} 0.1000000 1.0000000 4.285714 3
## [3705] {input,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [3706] {input,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [3707] {input,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3708] {input,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3709] {input,
## make} => {problem} 0.1000000 0.7500000 2.500000 3
## [3710] {input,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [3711] {input,
## make} => {method} 0.1000000 0.7500000 2.045455 3
## [3712] {input,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [3713] {input,
## make} => {approach} 0.1333333 1.0000000 2.500000 4
## [3714] {approach,
## input} => {make} 0.1333333 0.8000000 2.666667 4
## [3715] {input,
## make} => {perform} 0.1000000 0.7500000 1.607143 3
## [3716] {input,
## perform} => {make} 0.1000000 1.0000000 3.333333 3
## [3717] {input,
## make} => {represent} 0.1000000 0.7500000 1.500000 3
## [3718] {input,
## make} => {show} 0.1000000 0.7500000 1.406250 3
## [3719] {make,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [3720] {input,
## make} => {model} 0.1000000 0.7500000 1.406250 3
## [3721] {input,
## make} => {featur} 0.1000000 0.7500000 1.406250 3
## [3722] {featur,
## input} => {make} 0.1000000 0.7500000 2.500000 3
## [3723] {input,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [3724] {input,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [3725] {input,
## perform} => {problem} 0.1000000 1.0000000 3.333333 3
## [3726] {input,
## paper} => {represent} 0.1000000 0.7500000 1.500000 3
## [3727] {input,
## paper} => {show} 0.1000000 0.7500000 1.406250 3
## [3728] {input,
## paper} => {model} 0.1000000 0.7500000 1.406250 3
## [3729] {input,
## paper} => {network} 0.1000000 0.7500000 1.184211 3
## [3730] {input,
## network} => {paper} 0.1000000 0.7500000 2.250000 3
## [3731] {input,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [3732] {input,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [3733] {input,
## method} => {model} 0.1000000 1.0000000 1.875000 3
## [3734] {input,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [3735] {input,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [3736] {approach,
## input} => {represent} 0.1333333 0.8000000 1.600000 4
## [3737] {input,
## represent} => {approach} 0.1333333 0.8000000 2.000000 4
## [3738] {approach,
## input} => {show} 0.1333333 0.8000000 1.500000 4
## [3739] {input,
## show} => {approach} 0.1333333 0.8000000 2.000000 4
## [3740] {approach,
## input} => {model} 0.1333333 0.8000000 1.500000 4
## [3741] {input,
## model} => {approach} 0.1333333 0.8000000 2.000000 4
## [3742] {approach,
## input} => {featur} 0.1333333 0.8000000 1.500000 4
## [3743] {featur,
## input} => {approach} 0.1333333 1.0000000 2.500000 4
## [3744] {input,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [3745] {input,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [3746] {input,
## represent} => {show} 0.1333333 0.8000000 1.500000 4
## [3747] {input,
## show} => {represent} 0.1333333 0.8000000 1.600000 4
## [3748] {input,
## represent} => {model} 0.1333333 0.8000000 1.500000 4
## [3749] {input,
## model} => {represent} 0.1333333 0.8000000 1.600000 4
## [3750] {featur,
## input} => {represent} 0.1000000 0.7500000 1.500000 3
## [3751] {input,
## network} => {represent} 0.1000000 0.7500000 1.500000 3
## [3752] {input,
## show} => {model} 0.1666667 1.0000000 1.875000 5
## [3753] {input,
## model} => {show} 0.1666667 1.0000000 1.875000 5
## [3754] {featur,
## input} => {show} 0.1000000 0.7500000 1.406250 3
## [3755] {input,
## network} => {show} 0.1000000 0.7500000 1.406250 3
## [3756] {featur,
## input} => {model} 0.1000000 0.7500000 1.406250 3
## [3757] {input,
## network} => {model} 0.1000000 0.7500000 1.406250 3
## [3758] {comput,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [3759] {machin,
## comput} => {recent} 0.1000000 1.0000000 4.285714 3
## [3760] {reduc,
## comput} => {optim} 0.1000000 0.7500000 3.214286 3
## [3761] {comput,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [3762] {reduc,
## optim} => {comput} 0.1000000 0.7500000 3.214286 3
## [3763] {reduc,
## comput} => {problem} 0.1000000 0.7500000 2.500000 3
## [3764] {comput,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [3765] {reduc,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [3766] {reduc,
## comput} => {improv} 0.1000000 0.7500000 2.500000 3
## [3767] {improv,
## comput} => {reduc} 0.1000000 0.7500000 3.214286 3
## [3768] {reduc,
## improv} => {comput} 0.1000000 1.0000000 4.285714 3
## [3769] {reduc,
## comput} => {network} 0.1000000 0.7500000 1.184211 3
## [3770] {comput,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [3771] {comput,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [3772] {comput,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [3773] {improv,
## comput} => {optim} 0.1000000 0.7500000 3.214286 3
## [3774] {improv,
## optim} => {comput} 0.1000000 0.7500000 3.214286 3
## [3775] {comput,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [3776] {improv,
## comput} => {problem} 0.1000000 0.7500000 2.500000 3
## [3777] {improv,
## problem} => {comput} 0.1000000 0.7500000 3.214286 3
## [3778] {improv,
## comput} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3779] {algorithm,
## comput} => {improv} 0.1000000 0.7500000 2.500000 3
## [3780] {improv,
## comput} => {train} 0.1000000 0.7500000 1.875000 3
## [3781] {train,
## comput} => {improv} 0.1000000 1.0000000 3.333333 3
## [3782] {improv,
## comput} => {model} 0.1000000 0.7500000 1.406250 3
## [3783] {model,
## comput} => {improv} 0.1000000 0.7500000 2.500000 3
## [3784] {improv,
## comput} => {network} 0.1000000 0.7500000 1.184211 3
## [3785] {neural,
## comput} => {network} 0.1000000 1.0000000 1.578947 3
## [3786] {algorithm,
## comput} => {train} 0.1000000 0.7500000 1.875000 3
## [3787] {train,
## comput} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3788] {algorithm,
## comput} => {show} 0.1000000 0.7500000 1.406250 3
## [3789] {show,
## comput} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3790] {algorithm,
## comput} => {model} 0.1000000 0.7500000 1.406250 3
## [3791] {model,
## comput} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3792] {comput,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [3793] {problem,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [3794] {machin,
## problem} => {recent} 0.1000000 1.0000000 4.285714 3
## [3795] {algorithm,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [3796] {machin,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [3797] {machin,
## learn} => {recent} 0.1000000 0.7500000 3.214286 3
## [3798] {machin,
## recent} => {show} 0.1333333 0.8000000 1.500000 4
## [3799] {machin,
## show} => {recent} 0.1333333 0.8000000 3.428571 4
## [3800] {machin,
## recent} => {model} 0.1333333 0.8000000 1.500000 4
## [3801] {machin,
## model} => {recent} 0.1333333 0.8000000 3.428571 4
## [3802] {featur,
## recent} => {machin} 0.1000000 0.7500000 3.214286 3
## [3803] {recent,
## signific} => {problem} 0.1000000 1.0000000 3.333333 3
## [3804] {problem,
## recent} => {signific} 0.1000000 0.7500000 2.812500 3
## [3805] {problem,
## signific} => {recent} 0.1000000 1.0000000 4.285714 3
## [3806] {recent,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [3807] {recent,
## signific} => {model} 0.1000000 1.0000000 1.875000 3
## [3808] {model,
## signific} => {recent} 0.1000000 1.0000000 4.285714 3
## [3809] {problem,
## recent} => {method} 0.1000000 0.7500000 2.045455 3
## [3810] {method,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [3811] {problem,
## recent} => {perform} 0.1000000 0.7500000 1.607143 3
## [3812] {perform,
## recent} => {problem} 0.1000000 1.0000000 3.333333 3
## [3813] {problem,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [3814] {problem,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [3815] {problem,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [3816] {method,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3817] {method,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3818] {algorithm,
## recent} => {learn} 0.1000000 0.7500000 1.730769 3
## [3819] {algorithm,
## learn} => {recent} 0.1000000 1.0000000 4.285714 3
## [3820] {algorithm,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [3821] {algorithm,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [3822] {algorithm,
## recent} => {featur} 0.1000000 0.7500000 1.406250 3
## [3823] {featur,
## recent} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3824] {approach,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [3825] {approach,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3826] {approach,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3827] {perform,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [3828] {perform,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3829] {perform,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3830] {represent,
## recent} => {learn} 0.1000000 1.0000000 2.307692 3
## [3831] {learn,
## recent} => {show} 0.1666667 1.0000000 1.875000 5
## [3832] {show,
## recent} => {learn} 0.1666667 0.8333333 1.923077 5
## [3833] {learn,
## recent} => {model} 0.1666667 1.0000000 1.875000 5
## [3834] {model,
## recent} => {learn} 0.1666667 0.8333333 1.923077 5
## [3835] {learn,
## recent} => {featur} 0.1333333 0.8000000 1.500000 4
## [3836] {featur,
## recent} => {learn} 0.1333333 1.0000000 2.307692 4
## [3837] {represent,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [3838] {represent,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [3839] {show,
## recent} => {model} 0.2000000 1.0000000 1.875000 6
## [3840] {model,
## recent} => {show} 0.2000000 1.0000000 1.875000 6
## [3841] {featur,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [3842] {featur,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [3843] {machin,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [3844] {machin,
## train} => {signific} 0.1000000 0.7500000 2.812500 3
## [3845] {machin,
## make} => {classif} 0.1000000 1.0000000 3.750000 3
## [3846] {classif,
## machin} => {make} 0.1000000 0.7500000 2.500000 3
## [3847] {classif,
## make} => {machin} 0.1000000 1.0000000 4.285714 3
## [3848] {machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [3849] {machin,
## method} => {make} 0.1000000 0.7500000 2.500000 3
## [3850] {machin,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [3851] {approach,
## machin} => {make} 0.1000000 1.0000000 3.333333 3
## [3852] {machin,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [3853] {classif,
## machin} => {paper} 0.1000000 0.7500000 2.250000 3
## [3854] {machin,
## paper} => {classif} 0.1000000 1.0000000 3.750000 3
## [3855] {classif,
## paper} => {machin} 0.1000000 1.0000000 4.285714 3
## [3856] {classif,
## machin} => {method} 0.1000000 0.7500000 2.045455 3
## [3857] {machin,
## method} => {classif} 0.1000000 0.7500000 2.812500 3
## [3858] {classif,
## machin} => {task} 0.1000000 0.7500000 2.045455 3
## [3859] {machin,
## task} => {classif} 0.1000000 0.7500000 2.812500 3
## [3860] {classif,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [3861] {classif,
## machin} => {train} 0.1000000 0.7500000 1.875000 3
## [3862] {machin,
## train} => {classif} 0.1000000 0.7500000 2.812500 3
## [3863] {classif,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [3864] {classif,
## machin} => {approach} 0.1000000 0.7500000 1.875000 3
## [3865] {approach,
## machin} => {classif} 0.1000000 1.0000000 3.750000 3
## [3866] {classif,
## machin} => {learn} 0.1000000 0.7500000 1.730769 3
## [3867] {machin,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [3868] {classif,
## machin} => {show} 0.1000000 0.7500000 1.406250 3
## [3869] {classif,
## machin} => {model} 0.1000000 0.7500000 1.406250 3
## [3870] {classif,
## machin} => {featur} 0.1333333 1.0000000 1.875000 4
## [3871] {featur,
## machin} => {classif} 0.1333333 0.8000000 3.000000 4
## [3872] {machin,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [3873] {machin,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [3874] {machin,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [3875] {machin,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [3876] {machin,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [3877] {machin,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [3878] {machin,
## paper} => {featur} 0.1000000 1.0000000 1.875000 3
## [3879] {machin,
## method} => {train} 0.1000000 0.7500000 1.875000 3
## [3880] {machin,
## train} => {method} 0.1000000 0.7500000 2.045455 3
## [3881] {method,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [3882] {machin,
## method} => {approach} 0.1000000 0.7500000 1.875000 3
## [3883] {approach,
## machin} => {method} 0.1000000 1.0000000 2.727273 3
## [3884] {machin,
## method} => {show} 0.1000000 0.7500000 1.406250 3
## [3885] {machin,
## method} => {model} 0.1000000 0.7500000 1.406250 3
## [3886] {machin,
## method} => {featur} 0.1000000 0.7500000 1.406250 3
## [3887] {machin,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [3888] {machin,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [3889] {machin,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [3890] {machin,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [3891] {machin,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [3892] {data,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [3893] {machin,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [3894] {machin,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [3895] {machin,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [3896] {machin,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [3897] {machin,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [3898] {machin,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [3899] {machin,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [3900] {featur,
## machin} => {task} 0.1333333 0.8000000 2.181818 4
## [3901] {machin,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [3902] {machin,
## train} => {model} 0.1000000 0.7500000 1.406250 3
## [3903] {machin,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [3904] {approach,
## machin} => {featur} 0.1000000 1.0000000 1.875000 3
## [3905] {data,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [3906] {data,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [3907] {data,
## machin} => {featur} 0.1000000 1.0000000 1.875000 3
## [3908] {machin,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [3909] {machin,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [3910] {machin,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [3911] {featur,
## machin} => {learn} 0.1333333 0.8000000 1.846154 4
## [3912] {machin,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [3913] {machin,
## show} => {model} 0.1666667 1.0000000 1.875000 5
## [3914] {machin,
## model} => {show} 0.1666667 1.0000000 1.875000 5
## [3915] {machin,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [3916] {featur,
## machin} => {show} 0.1333333 0.8000000 1.500000 4
## [3917] {machin,
## model} => {featur} 0.1333333 0.8000000 1.500000 4
## [3918] {featur,
## machin} => {model} 0.1333333 0.8000000 1.500000 4
## [3919] {experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3920] {architectur,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [3921] {experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [3922] {classif,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [3923] {classif,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [3924] {experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3925] {dataset,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [3926] {dataset,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [3927] {experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [3928] {process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [3929] {experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [3930] {classif,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3931] {classif,
## architectur} => {process} 0.1000000 0.7500000 3.750000 3
## [3932] {process,
## recognit} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3933] {architectur,
## recognit} => {process} 0.1000000 1.0000000 5.000000 3
## [3934] {improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3935] {architectur,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [3936] {process,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3937] {architectur,
## result} => {process} 0.1000000 0.7500000 3.750000 3
## [3938] {neural,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3939] {architectur,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [3940] {architectur,
## process} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [3941] {algorithm,
## process} => {architectur} 0.1333333 0.8000000 3.000000 4
## [3942] {algorithm,
## architectur} => {process} 0.1333333 1.0000000 5.000000 4
## [3943] {process,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3944] {perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3945] {data,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3946] {data,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [3947] {architectur,
## process} => {dataset} 0.1333333 0.8000000 1.846154 4
## [3948] {dataset,
## process} => {architectur} 0.1333333 1.0000000 3.750000 4
## [3949] {architectur,
## dataset} => {process} 0.1333333 0.8000000 4.000000 4
## [3950] {show,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3951] {show,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [3952] {process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [3953] {model,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [3954] {model,
## architectur} => {process} 0.1000000 0.7500000 3.750000 3
## [3955] {architectur,
## process} => {featur} 0.1333333 0.8000000 1.500000 4
## [3956] {featur,
## process} => {architectur} 0.1333333 0.8000000 3.000000 4
## [3957] {architectur,
## process} => {network} 0.1333333 0.8000000 1.263158 4
## [3958] {network,
## process} => {architectur} 0.1333333 0.8000000 3.000000 4
## [3959] {classif,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3960] {dataset,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [3961] {classif,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [3962] {classif,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [3963] {process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [3964] {classif,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [3965] {process,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [3966] {model,
## process} => {recognit} 0.1000000 0.7500000 2.500000 3
## [3967] {model,
## recognit} => {process} 0.1000000 0.7500000 3.750000 3
## [3968] {process,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [3969] {improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [3970] {neural,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [3971] {improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3972] {improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [3973] {perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [3974] {improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [3975] {dataset,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [3976] {improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [3977] {process,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3978] {process,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [3979] {neural,
## process} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [3980] {algorithm,
## process} => {neural} 0.1333333 0.8000000 2.400000 4
## [3981] {algorithm,
## neural} => {process} 0.1333333 0.8000000 4.000000 4
## [3982] {neural,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [3983] {approach,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [3984] {neural,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [3985] {process,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [3986] {neural,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [3987] {perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [3988] {neural,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [3989] {neural,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [3990] {dataset,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [3991] {neural,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [3992] {show,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [3993] {neural,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [3994] {neural,
## process} => {network} 0.1333333 1.0000000 1.578947 4
## [3995] {network,
## process} => {neural} 0.1333333 0.8000000 2.400000 4
## [3996] {approach,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [3997] {process,
## work} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [3998] {algorithm,
## work} => {process} 0.1000000 0.7500000 3.750000 3
## [3999] {perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4000] {dataset,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4001] {algorithm,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [4002] {represent,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4003] {represent,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [4004] {algorithm,
## process} => {show} 0.1333333 0.8000000 1.500000 4
## [4005] {show,
## process} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [4006] {model,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4007] {algorithm,
## process} => {featur} 0.1333333 0.8000000 1.500000 4
## [4008] {featur,
## process} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [4009] {algorithm,
## process} => {network} 0.1333333 0.8000000 1.263158 4
## [4010] {network,
## process} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [4011] {approach,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [4012] {show,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [4013] {approach,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [4014] {process,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4015] {dataset,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [4016] {process,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [4017] {process,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [4018] {network,
## process} => {work} 0.1333333 0.8000000 2.000000 4
## [4019] {perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4020] {dataset,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [4021] {perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [4022] {data,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [4023] {dataset,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [4024] {process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4025] {dataset,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [4026] {dataset,
## process} => {network} 0.1333333 1.0000000 1.578947 4
## [4027] {network,
## process} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4028] {process,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [4029] {model,
## process} => {learn} 0.1000000 0.7500000 1.730769 3
## [4030] {process,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [4031] {represent,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [4032] {show,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [4033] {model,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [4034] {show,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [4035] {show,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [4036] {process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [4037] {model,
## process} => {featur} 0.1333333 1.0000000 1.875000 4
## [4038] {featur,
## process} => {model} 0.1333333 0.8000000 1.500000 4
## [4039] {model,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [4040] {featur,
## process} => {network} 0.1333333 0.8000000 1.263158 4
## [4041] {network,
## process} => {featur} 0.1333333 0.8000000 1.500000 4
## [4042] {experi,
## demonstr} => {network} 0.1000000 1.0000000 1.578947 3
## [4043] {network,
## demonstr} => {experi} 0.1000000 0.7500000 2.812500 3
## [4044] {classif,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4045] {dataset,
## demonstr} => {classif} 0.1000000 0.7500000 2.812500 3
## [4046] {classif,
## dataset} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [4047] {demonstr,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [4048] {approach,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [4049] {demonstr,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [4050] {perform,
## demonstr} => {problem} 0.1000000 0.7500000 2.500000 3
## [4051] {demonstr,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4052] {dataset,
## demonstr} => {problem} 0.1000000 0.7500000 2.500000 3
## [4053] {dataset,
## problem} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [4054] {improv,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [4055] {perform,
## demonstr} => {improv} 0.1000000 0.7500000 2.500000 3
## [4056] {improv,
## demonstr} => {show} 0.1000000 1.0000000 1.875000 3
## [4057] {show,
## demonstr} => {improv} 0.1000000 1.0000000 3.333333 3
## [4058] {task,
## demonstr} => {data} 0.1000000 1.0000000 2.307692 3
## [4059] {data,
## demonstr} => {task} 0.1000000 1.0000000 2.727273 3
## [4060] {approach,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [4061] {perform,
## demonstr} => {approach} 0.1000000 0.7500000 1.875000 3
## [4062] {approach,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4063] {dataset,
## demonstr} => {approach} 0.1000000 0.7500000 1.875000 3
## [4064] {demonstr,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [4065] {perform,
## demonstr} => {work} 0.1000000 0.7500000 1.875000 3
## [4066] {demonstr,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4067] {dataset,
## demonstr} => {work} 0.1000000 0.7500000 1.875000 3
## [4068] {demonstr,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [4069] {propos,
## demonstr} => {work} 0.1333333 1.0000000 2.500000 4
## [4070] {perform,
## demonstr} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4071] {dataset,
## demonstr} => {perform} 0.1000000 0.7500000 1.607143 3
## [4072] {perform,
## demonstr} => {show} 0.1000000 0.7500000 1.406250 3
## [4073] {show,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [4074] {perform,
## demonstr} => {propos} 0.1000000 0.7500000 1.500000 3
## [4075] {propos,
## demonstr} => {perform} 0.1000000 0.7500000 1.607143 3
## [4076] {dataset,
## demonstr} => {learn} 0.1000000 0.7500000 1.730769 3
## [4077] {demonstr,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4078] {dataset,
## demonstr} => {propos} 0.1000000 0.7500000 1.500000 3
## [4079] {propos,
## demonstr} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4080] {dataset,
## demonstr} => {model} 0.1000000 0.7500000 1.406250 3
## [4081] {model,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4082] {dataset,
## demonstr} => {featur} 0.1000000 0.7500000 1.406250 3
## [4083] {featur,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4084] {demonstr,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [4085] {model,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [4086] {demonstr,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [4087] {featur,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [4088] {model,
## demonstr} => {featur} 0.1000000 1.0000000 1.875000 3
## [4089] {featur,
## demonstr} => {model} 0.1000000 1.0000000 1.875000 3
## [4090] {reduc,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [4091] {reduc,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [4092] {task,
## achiev} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4093] {reduc,
## achiev} => {data} 0.1000000 1.0000000 2.307692 3
## [4094] {data,
## reduc} => {achiev} 0.1000000 0.7500000 3.214286 3
## [4095] {data,
## achiev} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4096] {reduc,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [4097] {reduc,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4098] {reduc,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [4099] {reduc,
## optim} => {improv} 0.1000000 0.7500000 2.500000 3
## [4100] {reduc,
## improv} => {optim} 0.1000000 1.0000000 4.285714 3
## [4101] {improv,
## optim} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4102] {reduc,
## optim} => {method} 0.1000000 0.7500000 2.045455 3
## [4103] {method,
## reduc} => {optim} 0.1000000 0.7500000 3.214286 3
## [4104] {method,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [4105] {reduc,
## optim} => {work} 0.1000000 0.7500000 1.875000 3
## [4106] {reduc,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [4107] {optim,
## work} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4108] {reduc,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4109] {show,
## optim} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4110] {reduc,
## optim} => {model} 0.1000000 0.7500000 1.406250 3
## [4111] {model,
## reduc} => {optim} 0.1000000 0.7500000 3.214286 3
## [4112] {model,
## optim} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4113] {reduc,
## optim} => {network} 0.1000000 0.7500000 1.184211 3
## [4114] {network,
## optim} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4115] {reduc,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [4116] {reduc,
## improv} => {problem} 0.1000000 1.0000000 3.333333 3
## [4117] {improv,
## problem} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4118] {paper,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [4119] {reduc,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [4120] {paper,
## reduc} => {data} 0.1000000 1.0000000 2.307692 3
## [4121] {data,
## reduc} => {paper} 0.1000000 0.7500000 2.250000 3
## [4122] {paper,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [4123] {method,
## reduc} => {train} 0.1000000 0.7500000 1.875000 3
## [4124] {reduc,
## train} => {method} 0.1000000 0.7500000 2.045455 3
## [4125] {method,
## train} => {reduc} 0.1000000 0.7500000 3.214286 3
## [4126] {method,
## reduc} => {approach} 0.1000000 0.7500000 1.875000 3
## [4127] {approach,
## reduc} => {method} 0.1000000 1.0000000 2.727273 3
## [4128] {method,
## reduc} => {show} 0.1333333 1.0000000 1.875000 4
## [4129] {reduc,
## show} => {method} 0.1333333 0.8000000 2.181818 4
## [4130] {method,
## reduc} => {model} 0.1000000 0.7500000 1.406250 3
## [4131] {model,
## reduc} => {method} 0.1000000 0.7500000 2.045455 3
## [4132] {method,
## reduc} => {network} 0.1000000 0.7500000 1.184211 3
## [4133] {reduc,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [4134] {data,
## reduc} => {task} 0.1333333 1.0000000 2.727273 4
## [4135] {reduc,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [4136] {reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [4137] {reduc,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [4138] {reduc,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [4139] {featur,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [4140] {reduc,
## task} => {network} 0.1333333 1.0000000 1.578947 4
## [4141] {reduc,
## train} => {show} 0.1333333 1.0000000 1.875000 4
## [4142] {reduc,
## show} => {train} 0.1333333 0.8000000 2.000000 4
## [4143] {reduc,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [4144] {approach,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [4145] {approach,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [4146] {reduc,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [4147] {data,
## reduc} => {represent} 0.1000000 0.7500000 1.500000 3
## [4148] {reduc,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [4149] {data,
## reduc} => {show} 0.1000000 0.7500000 1.406250 3
## [4150] {data,
## reduc} => {featur} 0.1000000 0.7500000 1.406250 3
## [4151] {featur,
## reduc} => {data} 0.1000000 1.0000000 2.307692 3
## [4152] {data,
## reduc} => {network} 0.1333333 1.0000000 1.578947 4
## [4153] {reduc,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [4154] {reduc,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [4155] {reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [4156] {reduc,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [4157] {featur,
## reduc} => {represent} 0.1000000 1.0000000 2.000000 3
## [4158] {reduc,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [4159] {model,
## reduc} => {show} 0.1000000 0.7500000 1.406250 3
## [4160] {featur,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [4161] {reduc,
## show} => {network} 0.1333333 0.8000000 1.263158 4
## [4162] {model,
## reduc} => {network} 0.1000000 0.7500000 1.184211 3
## [4163] {featur,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [4164] {paper,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [4165] {task,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [4166] {paper,
## achiev} => {train} 0.1000000 0.7500000 1.875000 3
## [4167] {train,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [4168] {paper,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [4169] {data,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [4170] {paper,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [4171] {achiev,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [4172] {paper,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [4173] {represent,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [4174] {paper,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4175] {paper,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4176] {achiev,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4177] {achiev,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [4178] {achiev,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [4179] {achiev,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [4180] {achiev,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4181] {achiev,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [4182] {achiev,
## improv} => {model} 0.1000000 1.0000000 1.875000 3
## [4183] {model,
## achiev} => {improv} 0.1000000 0.7500000 2.500000 3
## [4184] {achiev,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [4185] {achiev,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [4186] {achiev,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [4187] {train,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [4188] {achiev,
## result} => {approach} 0.1000000 0.7500000 1.875000 3
## [4189] {approach,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [4190] {achiev,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4191] {achiev,
## result} => {propos} 0.1000000 0.7500000 1.500000 3
## [4192] {achiev,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [4193] {achiev,
## result} => {network} 0.1333333 1.0000000 1.578947 4
## [4194] {achiev,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [4195] {train,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [4196] {achiev,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [4197] {approach,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [4198] {achiev,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4199] {achiev,
## neural} => {propos} 0.1333333 1.0000000 2.000000 4
## [4200] {achiev,
## propos} => {neural} 0.1333333 0.8000000 2.400000 4
## [4201] {achiev,
## neural} => {model} 0.1000000 0.7500000 1.406250 3
## [4202] {model,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [4203] {achiev,
## neural} => {featur} 0.1000000 0.7500000 1.406250 3
## [4204] {achiev,
## neural} => {network} 0.1000000 0.7500000 1.184211 3
## [4205] {method,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [4206] {approach,
## achiev} => {method} 0.1000000 0.7500000 2.045455 3
## [4207] {method,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [4208] {method,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [4209] {method,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [4210] {task,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [4211] {data,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [4212] {task,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [4213] {achiev,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [4214] {task,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [4215] {represent,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [4216] {task,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4217] {task,
## achiev} => {network} 0.1333333 1.0000000 1.578947 4
## [4218] {train,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4219] {train,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [4220] {achiev,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [4221] {train,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [4222] {represent,
## achiev} => {train} 0.1000000 0.7500000 1.875000 3
## [4223] {train,
## achiev} => {propos} 0.1000000 0.7500000 1.500000 3
## [4224] {train,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4225] {train,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4226] {approach,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4227] {approach,
## achiev} => {propos} 0.1333333 1.0000000 2.000000 4
## [4228] {achiev,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [4229] {approach,
## achiev} => {model} 0.1000000 0.7500000 1.406250 3
## [4230] {model,
## achiev} => {approach} 0.1000000 0.7500000 1.875000 3
## [4231] {approach,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4232] {approach,
## achiev} => {network} 0.1333333 1.0000000 1.578947 4
## [4233] {data,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4234] {data,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [4235] {achiev,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [4236] {data,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [4237] {represent,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [4238] {data,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4239] {data,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4240] {achiev,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4241] {represent,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4242] {show,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4243] {achiev,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [4244] {achiev,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4245] {achiev,
## dataset} => {model} 0.1333333 0.8000000 1.500000 4
## [4246] {model,
## achiev} => {dataset} 0.1333333 1.0000000 2.307692 4
## [4247] {achiev,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [4248] {featur,
## achiev} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4249] {achiev,
## dataset} => {network} 0.1333333 0.8000000 1.263158 4
## [4250] {achiev,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [4251] {represent,
## achiev} => {learn} 0.1333333 1.0000000 2.307692 4
## [4252] {achiev,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [4253] {achiev,
## learn} => {featur} 0.1333333 1.0000000 1.875000 4
## [4254] {featur,
## achiev} => {learn} 0.1333333 0.8000000 1.846154 4
## [4255] {achiev,
## learn} => {network} 0.1000000 0.7500000 1.184211 3
## [4256] {represent,
## achiev} => {propos} 0.1000000 0.7500000 1.500000 3
## [4257] {represent,
## achiev} => {featur} 0.1333333 1.0000000 1.875000 4
## [4258] {featur,
## achiev} => {represent} 0.1333333 0.8000000 1.600000 4
## [4259] {represent,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4260] {show,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [4261] {show,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [4262] {achiev,
## propos} => {model} 0.1333333 0.8000000 1.500000 4
## [4263] {model,
## achiev} => {propos} 0.1333333 1.0000000 2.000000 4
## [4264] {achiev,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [4265] {featur,
## achiev} => {propos} 0.1333333 0.8000000 1.600000 4
## [4266] {achiev,
## propos} => {network} 0.1333333 0.8000000 1.263158 4
## [4267] {model,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [4268] {model,
## achiev} => {network} 0.1000000 0.7500000 1.184211 3
## [4269] {featur,
## achiev} => {network} 0.1333333 0.8000000 1.263158 4
## [4270] {optim,
## signific} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4271] {algorithm,
## signific} => {optim} 0.1000000 1.0000000 4.285714 3
## [4272] {optim,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4273] {train,
## optim} => {signific} 0.1000000 0.7500000 2.812500 3
## [4274] {object,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4275] {object,
## problem} => {optim} 0.1000000 0.7500000 3.214286 3
## [4276] {object,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4277] {algorithm,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [4278] {object,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [4279] {task,
## optim} => {object} 0.1000000 1.0000000 3.750000 3
## [4280] {task,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [4281] {object,
## optim} => {data} 0.1000000 0.7500000 1.730769 3
## [4282] {data,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4283] {data,
## object} => {optim} 0.1000000 0.7500000 3.214286 3
## [4284] {object,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4285] {show,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4286] {object,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [4287] {propos,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4288] {object,
## optim} => {model} 0.1000000 0.7500000 1.406250 3
## [4289] {model,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4290] {object,
## optim} => {featur} 0.1000000 0.7500000 1.406250 3
## [4291] {featur,
## optim} => {object} 0.1000000 0.7500000 2.812500 3
## [4292] {architectur,
## optim} => {improv} 0.1000000 1.0000000 3.333333 3
## [4293] {improv,
## optim} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4294] {architectur,
## improv} => {optim} 0.1000000 0.7500000 3.214286 3
## [4295] {architectur,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [4296] {optim,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4297] {architectur,
## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [4298] {architectur,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [4299] {network,
## optim} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4300] {improv,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4301] {improv,
## problem} => {optim} 0.1000000 0.7500000 3.214286 3
## [4302] {optim,
## problem} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [4303] {algorithm,
## optim} => {problem} 0.1333333 0.8000000 2.666667 4
## [4304] {algorithm,
## problem} => {optim} 0.1333333 1.0000000 4.285714 4
## [4305] {train,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4306] {train,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [4307] {optim,
## problem} => {perform} 0.1333333 0.8000000 1.714286 4
## [4308] {perform,
## optim} => {problem} 0.1333333 0.8000000 2.666667 4
## [4309] {show,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4310] {propos,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4311] {model,
## optim} => {problem} 0.1000000 0.7500000 2.500000 3
## [4312] {improv,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4313] {improv,
## optim} => {train} 0.1000000 0.7500000 1.875000 3
## [4314] {train,
## optim} => {improv} 0.1000000 0.7500000 2.500000 3
## [4315] {improv,
## optim} => {work} 0.1000000 0.7500000 1.875000 3
## [4316] {optim,
## work} => {improv} 0.1000000 0.7500000 2.500000 3
## [4317] {improv,
## work} => {optim} 0.1000000 0.7500000 3.214286 3
## [4318] {improv,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4319] {improv,
## optim} => {network} 0.1000000 0.7500000 1.184211 3
## [4320] {network,
## optim} => {improv} 0.1000000 0.7500000 2.500000 3
## [4321] {method,
## optim} => {show} 0.1000000 1.0000000 1.875000 3
## [4322] {show,
## optim} => {method} 0.1000000 0.7500000 2.045455 3
## [4323] {algorithm,
## optim} => {train} 0.1333333 0.8000000 2.000000 4
## [4324] {train,
## optim} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [4325] {algorithm,
## optim} => {perform} 0.1333333 0.8000000 1.714286 4
## [4326] {perform,
## optim} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [4327] {data,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4328] {show,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4329] {propos,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4330] {featur,
## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4331] {task,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [4332] {data,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [4333] {task,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [4334] {propos,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [4335] {task,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [4336] {featur,
## optim} => {task} 0.1000000 0.7500000 2.045455 3
## [4337] {train,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4338] {train,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4339] {show,
## optim} => {train} 0.1000000 0.7500000 1.875000 3
## [4340] {approach,
## optim} => {propos} 0.1000000 1.0000000 2.000000 3
## [4341] {propos,
## optim} => {approach} 0.1000000 0.7500000 1.875000 3
## [4342] {optim,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [4343] {optim,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4344] {dataset,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [4345] {optim,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [4346] {network,
## optim} => {work} 0.1333333 1.0000000 2.500000 4
## [4347] {data,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4348] {propos,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4349] {featur,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4350] {network,
## optim} => {perform} 0.1000000 0.7500000 1.607143 3
## [4351] {data,
## optim} => {represent} 0.1000000 0.7500000 1.500000 3
## [4352] {represent,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [4353] {data,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [4354] {propos,
## optim} => {data} 0.1000000 0.7500000 1.730769 3
## [4355] {data,
## optim} => {featur} 0.1333333 1.0000000 1.875000 4
## [4356] {featur,
## optim} => {data} 0.1333333 1.0000000 2.307692 4
## [4357] {dataset,
## optim} => {network} 0.1000000 1.0000000 1.578947 3
## [4358] {network,
## optim} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4359] {represent,
## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [4360] {featur,
## optim} => {represent} 0.1000000 0.7500000 1.500000 3
## [4361] {show,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [4362] {propos,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4363] {show,
## optim} => {model} 0.1000000 0.7500000 1.406250 3
## [4364] {model,
## optim} => {show} 0.1000000 0.7500000 1.406250 3
## [4365] {propos,
## optim} => {featur} 0.1000000 0.7500000 1.406250 3
## [4366] {featur,
## optim} => {propos} 0.1000000 0.7500000 1.500000 3
## [4367] {object,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4368] {train,
## object} => {signific} 0.1000000 1.0000000 3.750000 3
## [4369] {object,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [4370] {architectur,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [4371] {architectur,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [4372] {network,
## signific} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4373] {problem,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [4374] {problem,
## signific} => {model} 0.1000000 1.0000000 1.875000 3
## [4375] {model,
## signific} => {problem} 0.1000000 1.0000000 3.333333 3
## [4376] {paper,
## signific} => {task} 0.1000000 0.7500000 2.045455 3
## [4377] {task,
## signific} => {paper} 0.1000000 1.0000000 3.000000 3
## [4378] {paper,
## signific} => {train} 0.1000000 0.7500000 1.875000 3
## [4379] {paper,
## signific} => {learn} 0.1333333 1.0000000 2.307692 4
## [4380] {learn,
## signific} => {paper} 0.1333333 1.0000000 3.000000 4
## [4381] {paper,
## learn} => {signific} 0.1333333 0.8000000 3.000000 4
## [4382] {paper,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [4383] {paper,
## signific} => {show} 0.1000000 0.7500000 1.406250 3
## [4384] {paper,
## signific} => {propos} 0.1000000 0.7500000 1.500000 3
## [4385] {paper,
## propos} => {signific} 0.1000000 0.7500000 2.812500 3
## [4386] {paper,
## signific} => {featur} 0.1000000 0.7500000 1.406250 3
## [4387] {improv,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4388] {result,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4389] {result,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [4390] {result,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [4391] {result,
## signific} => {network} 0.1000000 1.0000000 1.578947 3
## [4392] {network,
## signific} => {result} 0.1000000 0.7500000 2.250000 3
## [4393] {method,
## signific} => {propos} 0.1000000 0.7500000 1.500000 3
## [4394] {algorithm,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4395] {task,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4396] {task,
## signific} => {learn} 0.1000000 1.0000000 2.307692 3
## [4397] {learn,
## signific} => {task} 0.1000000 0.7500000 2.045455 3
## [4398] {task,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [4399] {data,
## signific} => {train} 0.1000000 1.0000000 2.500000 3
## [4400] {learn,
## signific} => {train} 0.1000000 0.7500000 1.875000 3
## [4401] {represent,
## signific} => {train} 0.1333333 0.8000000 2.000000 4
## [4402] {show,
## signific} => {train} 0.1333333 0.8000000 2.000000 4
## [4403] {featur,
## signific} => {train} 0.1333333 0.8000000 2.000000 4
## [4404] {network,
## signific} => {train} 0.1000000 0.7500000 1.875000 3
## [4405] {work,
## signific} => {perform} 0.1000000 1.0000000 2.142857 3
## [4406] {dataset,
## signific} => {perform} 0.1000000 0.7500000 1.607143 3
## [4407] {perform,
## signific} => {propos} 0.1333333 0.8000000 1.600000 4
## [4408] {propos,
## signific} => {perform} 0.1333333 0.8000000 1.714286 4
## [4409] {data,
## signific} => {featur} 0.1000000 1.0000000 1.875000 3
## [4410] {dataset,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [4411] {dataset,
## signific} => {featur} 0.1000000 0.7500000 1.406250 3
## [4412] {dataset,
## signific} => {network} 0.1000000 0.7500000 1.184211 3
## [4413] {network,
## signific} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4414] {learn,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [4415] {learn,
## signific} => {show} 0.1000000 0.7500000 1.406250 3
## [4416] {learn,
## signific} => {propos} 0.1000000 0.7500000 1.500000 3
## [4417] {learn,
## signific} => {featur} 0.1000000 0.7500000 1.406250 3
## [4418] {network,
## signific} => {represent} 0.1000000 0.7500000 1.500000 3
## [4419] {model,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [4420] {featur,
## signific} => {network} 0.1333333 0.8000000 1.263158 4
## [4421] {network,
## signific} => {featur} 0.1333333 1.0000000 1.875000 4
## [4422] {experi,
## success} => {paper} 0.1000000 1.0000000 3.000000 3
## [4423] {paper,
## success} => {experi} 0.1000000 1.0000000 3.750000 3
## [4424] {paper,
## experi} => {success} 0.1000000 0.7500000 2.812500 3
## [4425] {object,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4426] {represent,
## object} => {success} 0.1000000 0.7500000 2.812500 3
## [4427] {object,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [4428] {object,
## success} => {featur} 0.1000000 1.0000000 1.875000 3
## [4429] {make,
## success} => {approach} 0.1000000 1.0000000 2.500000 3
## [4430] {approach,
## success} => {make} 0.1000000 0.7500000 2.500000 3
## [4431] {make,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4432] {make,
## success} => {propos} 0.1000000 1.0000000 2.000000 3
## [4433] {problem,
## success} => {perform} 0.1000000 1.0000000 2.142857 3
## [4434] {perform,
## success} => {problem} 0.1000000 1.0000000 3.333333 3
## [4435] {problem,
## success} => {represent} 0.1000000 1.0000000 2.000000 3
## [4436] {represent,
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## [4437] {problem,
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## [4438] {method,
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## [4439] {approach,
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## [4440] {method,
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## [4441] {learn,
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## [4442] {method,
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## [4443] {method,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [4444] {method,
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## [4445] {method,
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## [4446] {show,
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## [4447] {method,
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## [4448] {propos,
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## [4449] {method,
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## [4451] {approach,
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## [4452] {task,
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## [4453] {data,
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## [4454] {task,
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## [4455] {task,
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## [4456] {task,
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## [4457] {task,
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## [4458] {featur,
## success} => {task} 0.1333333 0.8000000 2.181818 4
## [4459] {approach,
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## [4460] {approach,
## success} => {represent} 0.1333333 1.0000000 2.000000 4
## [4461] {approach,
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## [4462] {propos,
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## [4463] {approach,
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## [4464] {perform,
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## [4465] {perform,
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## [4466] {data,
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## [4467] {data,
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## [4468] {learn,
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## [4469] {represent,
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## [4470] {learn,
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## [4471] {show,
## success} => {learn} 0.1333333 1.0000000 2.307692 4
## [4472] {learn,
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## [4473] {propos,
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## [4475] {learn,
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## [4476] {featur,
## success} => {learn} 0.1333333 0.8000000 1.846154 4
## [4477] {show,
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## [4478] {represent,
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## [4479] {propos,
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## [4481] {represent,
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## [4482] {featur,
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## [4485] {model,
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## [4486] {show,
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## [4487] {propos,
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## [4488] {featur,
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## [4490] {classif,
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## [4491] {classif,
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## [4492] {architectur,
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## [4493] {experi,
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## [4494] {architectur,
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## [4495] {architectur,
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## [4496] {method,
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## [4497] {architectur,
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## [4498] {approach,
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## [4499] {approach,
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## [4500] {architectur,
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## [4501] {dataset,
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## [4502] {architectur,
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## [4503] {experi,
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## [4504] {architectur,
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## [4505] {architectur,
## experi} => {featur} 0.1000000 0.7500000 1.406250 3
## [4506] {featur,
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## [4507] {architectur,
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## [4508] {make,
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## [4509] {paper,
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## [4510] {make,
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## [4511] {classif,
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## [4512] {experi,
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## [4513] {classif,
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## [4514] {classif,
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## [4515] {classif,
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## [4516] {approach,
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## [4517] {classif,
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## [4518] {dataset,
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## [4520] {classif,
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## [4521] {experi,
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## [4522] {classif,
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## [4523] {featur,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [4524] {classif,
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## [4525] {classif,
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## [4526] {paper,
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## [4527] {experi,
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## [4528] {paper,
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## [4529] {paper,
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## [4530] {method,
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## [4531] {experi,
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## [4532] {method,
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## [4533] {experi,
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## [4534] {algorithm,
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## [4535] {experi,
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## [4536] {experi,
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## [4537] {method,
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## [4538] {experi,
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## [4539] {approach,
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## [4540] {experi,
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## [4541] {experi,
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## [4542] {algorithm,
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## [4543] {method,
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## [4544] {method,
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## [4545] {approach,
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## [4546] {method,
## experi} => {perform} 0.1333333 0.8000000 1.714286 4
## [4547] {experi,
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## [4548] {dataset,
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## [4549] {method,
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## [4550] {show,
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## [4551] {method,
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## [4552] {experi,
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## [4553] {model,
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## [4554] {algorithm,
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## [4555] {experi,
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## [4556] {algorithm,
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## [4557] {show,
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## [4558] {algorithm,
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## [4559] {train,
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## [4561] {experi,
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## [4564] {approach,
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## [4565] {show,
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## [4566] {approach,
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## [4568] {approach,
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## [4569] {experi,
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## [4570] {experi,
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## [4572] {experi,
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## [4573] {show,
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## [4574] {experi,
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## [4575] {experi,
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## [4576] {model,
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## [4578] {show,
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## [4579] {dataset,
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## [4580] {experi,
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## [4584] {experi,
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## [4588] {model,
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## [4589] {featur,
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## [4591] {featur,
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## [4593] {object,
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## [4601] {classif,
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## [4607] {object,
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## [4608] {object,
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## [4609] {object,
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## [4610] {perform,
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## [4611] {object,
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## [4612] {object,
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## [4613] {object,
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## [4616] {method,
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## [4617] {method,
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## [4618] {method,
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## [4620] {algorithm,
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## [4621] {algorithm,
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## [4622] {algorithm,
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## [4623] {algorithm,
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## [4625] {data,
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## [4626] {task,
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## [4627] {object,
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## [4630] {task,
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## [4638] {object,
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## [4644] {data,
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## [4648] {dataset,
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## [4649] {network,
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## [4650] {object,
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## [4653] {object,
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## [4654] {represent,
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## [4658] {object,
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## [4668] {architectur,
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## [4669] {classif,
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## [4670] {classif,
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## [4671] {method,
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## [4672] {classif,
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## [4673] {approach,
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## [4675] {classif,
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## [4676] {classif,
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## [4680] {classif,
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## [4681] {architectur,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [4682] {model,
## architectur} => {recognit} 0.1000000 0.7500000 2.500000 3
## [4683] {model,
## recognit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4684] {architectur,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [4685] {architectur,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [4686] {architectur,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [4687] {architectur,
## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4688] {algorithm,
## architectur} => {improv} 0.1000000 0.7500000 2.500000 3
## [4689] {architectur,
## improv} => {work} 0.1000000 0.7500000 1.875000 3
## [4690] {improv,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4691] {architectur,
## improv} => {perform} 0.1333333 1.0000000 2.142857 4
## [4692] {architectur,
## perform} => {improv} 0.1333333 0.8000000 2.666667 4
## [4693] {architectur,
## improv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4694] {architectur,
## improv} => {network} 0.1333333 1.0000000 1.578947 4
## [4695] {network,
## improv} => {architectur} 0.1333333 0.8000000 3.000000 4
## [4696] {architectur,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [4697] {architectur,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [4698] {architectur,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4699] {algorithm,
## architectur} => {result} 0.1000000 0.7500000 2.250000 3
## [4700] {architectur,
## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [4701] {represent,
## architectur} => {result} 0.1000000 1.0000000 3.000000 3
## [4702] {architectur,
## result} => {featur} 0.1333333 1.0000000 1.875000 4
## [4703] {architectur,
## result} => {network} 0.1000000 0.7500000 1.184211 3
## [4704] {architectur,
## neural} => {method} 0.1000000 0.7500000 2.045455 3
## [4705] {method,
## architectur} => {neural} 0.1000000 0.7500000 2.250000 3
## [4706] {method,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [4707] {architectur,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [4708] {algorithm,
## architectur} => {neural} 0.1000000 0.7500000 2.250000 3
## [4709] {architectur,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [4710] {train,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [4711] {architectur,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [4712] {approach,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [4713] {architectur,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [4714] {neural,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4715] {architectur,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4716] {architectur,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [4717] {architectur,
## neural} => {featur} 0.1000000 0.7500000 1.406250 3
## [4718] {architectur,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [4719] {method,
## architectur} => {approach} 0.1000000 0.7500000 1.875000 3
## [4720] {approach,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [4721] {method,
## architectur} => {perform} 0.1000000 0.7500000 1.607143 3
## [4722] {method,
## architectur} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4723] {method,
## architectur} => {propos} 0.1333333 1.0000000 2.000000 4
## [4724] {architectur,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [4725] {method,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [4726] {method,
## architectur} => {network} 0.1333333 1.0000000 1.578947 4
## [4727] {algorithm,
## architectur} => {perform} 0.1000000 0.7500000 1.607143 3
## [4728] {algorithm,
## architectur} => {dataset} 0.1000000 0.7500000 1.730769 3
## [4729] {algorithm,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [4730] {algorithm,
## architectur} => {show} 0.1000000 0.7500000 1.406250 3
## [4731] {show,
## architectur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [4732] {algorithm,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [4733] {algorithm,
## architectur} => {network} 0.1000000 0.7500000 1.184211 3
## [4734] {task,
## architectur} => {learn} 0.1000000 1.0000000 2.307692 3
## [4735] {architectur,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [4736] {task,
## architectur} => {featur} 0.1000000 1.0000000 1.875000 3
## [4737] {train,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [4738] {approach,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [4739] {approach,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [4740] {architectur,
## work} => {perform} 0.1333333 0.8000000 1.714286 4
## [4741] {architectur,
## perform} => {work} 0.1333333 0.8000000 2.000000 4
## [4742] {architectur,
## work} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4743] {architectur,
## dataset} => {work} 0.1333333 0.8000000 2.000000 4
## [4744] {architectur,
## work} => {network} 0.1666667 1.0000000 1.578947 5
## [4745] {network,
## architectur} => {work} 0.1666667 0.7142857 1.785714 5
## [4746] {architectur,
## perform} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4747] {architectur,
## dataset} => {perform} 0.1333333 0.8000000 1.714286 4
## [4748] {architectur,
## perform} => {network} 0.1666667 1.0000000 1.578947 5
## [4749] {network,
## architectur} => {perform} 0.1666667 0.7142857 1.530612 5
## [4750] {network,
## perform} => {architectur} 0.1666667 1.0000000 3.750000 5
## [4751] {data,
## architectur} => {featur} 0.1000000 1.0000000 1.875000 3
## [4752] {architectur,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [4753] {architectur,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4754] {architectur,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [4755] {architectur,
## dataset} => {network} 0.1666667 1.0000000 1.578947 5
## [4756] {network,
## architectur} => {dataset} 0.1666667 0.7142857 1.648352 5
## [4757] {architectur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [4758] {represent,
## architectur} => {featur} 0.1000000 1.0000000 1.875000 3
## [4759] {architectur,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [4760] {architectur,
## propos} => {network} 0.1666667 1.0000000 1.578947 5
## [4761] {network,
## architectur} => {propos} 0.1666667 0.7142857 1.428571 5
## [4762] {model,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [4763] {model,
## architectur} => {network} 0.1000000 0.7500000 1.184211 3
## [4764] {featur,
## architectur} => {network} 0.1666667 0.8333333 1.315789 5
## [4765] {network,
## architectur} => {featur} 0.1666667 0.7142857 1.339286 5
## [4766] {classif,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [4767] {classif,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [4768] {classif,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [4769] {make,
## problem} => {approach} 0.1333333 0.8000000 2.000000 4
## [4770] {approach,
## problem} => {make} 0.1333333 0.8000000 2.666667 4
## [4771] {make,
## problem} => {perform} 0.1666667 1.0000000 2.142857 5
## [4772] {make,
## perform} => {problem} 0.1666667 0.7142857 2.380952 5
## [4773] {make,
## dataset} => {problem} 0.1000000 0.7500000 2.500000 3
## [4774] {dataset,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [4775] {represent,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [4776] {make,
## problem} => {model} 0.1333333 0.8000000 1.500000 4
## [4777] {make,
## paper} => {method} 0.1333333 0.8000000 2.181818 4
## [4778] {make,
## method} => {paper} 0.1333333 0.8000000 2.400000 4
## [4779] {method,
## paper} => {make} 0.1333333 1.0000000 3.333333 4
## [4780] {make,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [4781] {approach,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [4782] {paper,
## perform} => {make} 0.1000000 0.7500000 2.500000 3
## [4783] {make,
## paper} => {represent} 0.1333333 0.8000000 1.600000 4
## [4784] {make,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [4785] {paper,
## propos} => {make} 0.1000000 0.7500000 2.500000 3
## [4786] {make,
## paper} => {model} 0.1333333 0.8000000 1.500000 4
## [4787] {make,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [4788] {make,
## improv} => {model} 0.1000000 1.0000000 1.875000 3
## [4789] {make,
## method} => {approach} 0.1333333 0.8000000 2.000000 4
## [4790] {make,
## method} => {show} 0.1333333 0.8000000 1.500000 4
## [4791] {make,
## show} => {method} 0.1333333 1.0000000 2.727273 4
## [4792] {make,
## method} => {model} 0.1333333 0.8000000 1.500000 4
## [4793] {make,
## task} => {approach} 0.1333333 1.0000000 2.500000 4
## [4794] {make,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [4795] {make,
## task} => {represent} 0.1333333 1.0000000 2.000000 4
## [4796] {make,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [4797] {make,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [4798] {make,
## train} => {represent} 0.1000000 1.0000000 2.000000 3
## [4799] {make,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [4800] {make,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [4801] {make,
## learn} => {approach} 0.1333333 0.8000000 2.000000 4
## [4802] {approach,
## make} => {represent} 0.1666667 0.8333333 1.666667 5
## [4803] {make,
## represent} => {approach} 0.1666667 0.8333333 2.083333 5
## [4804] {make,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [4805] {make,
## propos} => {approach} 0.1333333 0.8000000 2.000000 4
## [4806] {approach,
## make} => {featur} 0.1666667 0.8333333 1.562500 5
## [4807] {featur,
## make} => {approach} 0.1666667 0.8333333 2.083333 5
## [4808] {data,
## make} => {perform} 0.1333333 0.8000000 1.714286 4
## [4809] {make,
## dataset} => {perform} 0.1333333 1.0000000 2.142857 4
## [4810] {make,
## learn} => {perform} 0.1333333 0.8000000 1.714286 4
## [4811] {make,
## show} => {perform} 0.1000000 0.7500000 1.607143 3
## [4812] {make,
## propos} => {perform} 0.1333333 0.8000000 1.714286 4
## [4813] {make,
## perform} => {model} 0.2000000 0.8571429 1.607143 6
## [4814] {make,
## model} => {perform} 0.2000000 0.8571429 1.836735 6
## [4815] {model,
## perform} => {make} 0.2000000 0.7500000 2.500000 6
## [4816] {data,
## make} => {represent} 0.1333333 0.8000000 1.600000 4
## [4817] {data,
## make} => {model} 0.1333333 0.8000000 1.500000 4
## [4818] {data,
## make} => {featur} 0.1333333 0.8000000 1.500000 4
## [4819] {make,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [4820] {make,
## learn} => {dataset} 0.1333333 0.8000000 1.846154 4
## [4821] {make,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [4822] {make,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [4823] {make,
## dataset} => {model} 0.1333333 1.0000000 1.875000 4
## [4824] {make,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [4825] {make,
## learn} => {represent} 0.1333333 0.8000000 1.600000 4
## [4826] {make,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [4827] {make,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [4828] {make,
## learn} => {model} 0.1333333 0.8000000 1.500000 4
## [4829] {make,
## learn} => {featur} 0.1333333 0.8000000 1.500000 4
## [4830] {make,
## represent} => {propos} 0.1666667 0.8333333 1.666667 5
## [4831] {make,
## propos} => {represent} 0.1666667 1.0000000 2.000000 5
## [4832] {make,
## represent} => {featur} 0.1666667 0.8333333 1.562500 5
## [4833] {featur,
## make} => {represent} 0.1666667 0.8333333 1.666667 5
## [4834] {make,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [4835] {make,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [4836] {classif,
## problem} => {method} 0.1000000 0.7500000 2.045455 3
## [4837] {classif,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [4838] {classif,
## perform} => {problem} 0.1333333 0.8000000 2.666667 4
## [4839] {classif,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [4840] {classif,
## problem} => {show} 0.1333333 1.0000000 1.875000 4
## [4841] {classif,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [4842] {classif,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [4843] {classif,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [4844] {classif,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [4845] {classif,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [4846] {classif,
## train} => {paper} 0.1000000 0.7500000 2.250000 3
## [4847] {classif,
## paper} => {featur} 0.1000000 1.0000000 1.875000 3
## [4848] {classif,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [4849] {classif,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [4850] {classif,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [4851] {method,
## improv} => {classif} 0.1000000 0.7500000 2.812500 3
## [4852] {classif,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [4853] {approach,
## improv} => {classif} 0.1000000 0.7500000 2.812500 3
## [4854] {classif,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [4855] {classif,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [4856] {classif,
## dataset} => {improv} 0.1000000 0.7500000 2.500000 3
## [4857] {classif,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [4858] {classif,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [4859] {method,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [4860] {classif,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [4861] {classif,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [4862] {classif,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [4863] {classif,
## train} => {method} 0.1000000 0.7500000 2.045455 3
## [4864] {method,
## train} => {classif} 0.1000000 0.7500000 2.812500 3
## [4865] {classif,
## method} => {approach} 0.1666667 0.8333333 2.083333 5
## [4866] {approach,
## classif} => {method} 0.1666667 1.0000000 2.727273 5
## [4867] {approach,
## method} => {classif} 0.1666667 0.7142857 2.678571 5
## [4868] {classif,
## perform} => {method} 0.1333333 0.8000000 2.181818 4
## [4869] {classif,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [4870] {classif,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [4871] {classif,
## method} => {show} 0.1666667 0.8333333 1.562500 5
## [4872] {classif,
## show} => {method} 0.1666667 0.8333333 2.272727 5
## [4873] {classif,
## method} => {featur} 0.1666667 0.8333333 1.562500 5
## [4874] {classif,
## featur} => {method} 0.1666667 0.7142857 1.948052 5
## [4875] {featur,
## method} => {classif} 0.1666667 0.7142857 2.678571 5
## [4876] {classif,
## network} => {method} 0.1333333 0.8000000 2.181818 4
## [4877] {classif,
## algorithm} => {perform} 0.1000000 1.0000000 2.142857 3
## [4878] {classif,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [4879] {classif,
## algorithm} => {propos} 0.1000000 1.0000000 2.000000 3
## [4880] {classif,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [4881] {classif,
## train} => {task} 0.1000000 0.7500000 2.045455 3
## [4882] {classif,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [4883] {classif,
## data} => {task} 0.1000000 1.0000000 2.727273 3
## [4884] {classif,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [4885] {classif,
## task} => {propos} 0.1000000 0.7500000 1.500000 3
## [4886] {classif,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [4887] {classif,
## task} => {featur} 0.1333333 1.0000000 1.875000 4
## [4888] {classif,
## task} => {network} 0.1000000 0.7500000 1.184211 3
## [4889] {classif,
## train} => {approach} 0.1000000 0.7500000 1.875000 3
## [4890] {classif,
## train} => {show} 0.1000000 0.7500000 1.406250 3
## [4891] {classif,
## train} => {propos} 0.1000000 0.7500000 1.500000 3
## [4892] {classif,
## train} => {featur} 0.1000000 0.7500000 1.406250 3
## [4893] {classif,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [4894] {classif,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [4895] {approach,
## classif} => {show} 0.1333333 0.8000000 1.500000 4
## [4896] {approach,
## classif} => {featur} 0.1333333 0.8000000 1.500000 4
## [4897] {approach,
## classif} => {network} 0.1333333 0.8000000 1.263158 4
## [4898] {classif,
## network} => {approach} 0.1333333 0.8000000 2.000000 4
## [4899] {classif,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [4900] {classif,
## perform} => {show} 0.1666667 1.0000000 1.875000 5
## [4901] {classif,
## show} => {perform} 0.1666667 0.8333333 1.785714 5
## [4902] {classif,
## perform} => {propos} 0.1333333 0.8000000 1.600000 4
## [4903] {classif,
## perform} => {featur} 0.1333333 0.8000000 1.500000 4
## [4904] {classif,
## data} => {model} 0.1000000 1.0000000 1.875000 3
## [4905] {classif,
## data} => {featur} 0.1000000 1.0000000 1.875000 3
## [4906] {classif,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [4907] {classif,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [4908] {classif,
## dataset} => {model} 0.1000000 0.7500000 1.406250 3
## [4909] {classif,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [4910] {classif,
## dataset} => {network} 0.1000000 0.7500000 1.184211 3
## [4911] {classif,
## learn} => {propos} 0.1333333 0.8000000 1.600000 4
## [4912] {classif,
## learn} => {featur} 0.1666667 1.0000000 1.875000 5
## [4913] {classif,
## featur} => {learn} 0.1666667 0.7142857 1.648352 5
## [4914] {classif,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [4915] {classif,
## model} => {show} 0.1333333 0.8000000 1.500000 4
## [4916] {classif,
## show} => {featur} 0.1666667 0.8333333 1.562500 5
## [4917] {classif,
## featur} => {show} 0.1666667 0.7142857 1.339286 5
## [4918] {classif,
## propos} => {featur} 0.1666667 0.8333333 1.562500 5
## [4919] {classif,
## featur} => {propos} 0.1666667 0.7142857 1.428571 5
## [4920] {classif,
## network} => {propos} 0.1333333 0.8000000 1.600000 4
## [4921] {classif,
## model} => {featur} 0.1666667 1.0000000 1.875000 5
## [4922] {classif,
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## [5644] {represent,
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## [5645] {recognit,
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## [5646] {train,
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## [5647] {train,
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## [5648] {train,
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## [5649] {recognit,
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## [5650] {represent,
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## [5651] {represent,
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## [5652] {represent,
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## [5654] {represent,
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## [5655] {represent,
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## [5656] {represent,
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## [5657] {train,
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## [5658] {represent,
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## [5659] {represent,
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## [5660] {represent,
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## [5662] {make,
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## [5663] {perform,
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## [5664] {make,
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## [5667] {make,
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## [5668] {make,
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## [5670] {model,
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## [5676] {featur,
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## [5678] {perform,
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## [5679] {improv,
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## [5680] {improv,
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## [5681] {improv,
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## [5682] {dataset,
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## [5683] {dataset,
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## [5684] {dataset,
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## [5685] {improv,
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## [5686] {featur,
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## [5687] {featur,
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## [5688] {featur,
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## [5689] {perform,
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## [5690] {dataset,
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## [5691] {dataset,
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## [5693] {perform,
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## [5694] {featur,
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## [5699] {featur,
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## [5700] {featur,
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## [5702] {dataset,
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## [5703] {dataset,
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## [5704] {improv,
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## [5705] {featur,
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## [5706] {featur,
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## [5707] {featur,
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## [5708] {dataset,
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## [5709] {featur,
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## [5710] {featur,
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## [5711] {featur,
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## [5712] {dataset,
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## [5713] {featur,
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## [5714] {featur,
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## [5715] {represent,
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## [5716] {perform,
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## [5725] {featur,
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## [5728] {featur,
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## [5729] {featur,
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## [5731] {advantag,
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## [5732] {advantag,
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## [5733] {advantag,
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## [5734] {advantag,
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## [5735] {advantag,
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## [5736] {advantag,
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## [5737] {advantag,
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## [5738] {advantag,
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## [5739] {classif,
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## [5740] {advantag,
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## [5741] {advantag,
## classif,
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## [5742] {advantag,
## approach,
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## [5743] {approach,
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## [5744] {advantag,
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## [5745] {advantag,
## classif,
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## [5746] {advantag,
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## [5747] {approach,
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## [5748] {advantag,
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## [5749] {advantag,
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## [5750] {advantag,
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## [5751] {classif,
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## [5753] {advantag,
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## [5755] {advantag,
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## [5756] {advantag,
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## [5758] {advantag,
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## [5759] {advantag,
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## [5760] {advantag,
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## [5763] {advantag,
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## [5765] {network,
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## [5767] {network,
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## [5769] {featur,
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## [5770] {featur,
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## [5771] {featur,
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## [5773] {work,
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## [5774] {train,
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## [5775] {train,
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## [5776] {perform,
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## [5777] {show,
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## [5778] {show,
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## [5779] {show,
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## [5780] {perform,
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## [5783] {perform,
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## [5790] {show,
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## local} => {perform} 0.1000000 1.0000000 2.142857 3
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## [5792] {machin,
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## [5793] {classif,
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## [5794] {classif,
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## [5795] {classif,
## machin,
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## [5796] {featur,
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## [5797] {classif,
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## [5798] {classif,
## featur,
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## [5799] {machin,
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## [5800] {featur,
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## [5801] {featur,
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## [5802] {featur,
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## [5807] {featur,
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## [5808] {featur,
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## [5810] {data,
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## [5811] {data,
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## [5812] {data,
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## [5814] {present,
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## [5815] {data,
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## [5816] {data,
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## [5822] {data,
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## [5826] {train,
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## [5828] {approach,
## show,
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## [5829] {show,
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## [5830] {approach,
## dataset,
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## [5831] {approach,
## propos,
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## [5832] {dataset,
## propos,
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## propos,
## common} => {show} 0.1000000 1.0000000 1.875000 3
## [5835] {show,
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## [5838] {show,
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## [5839] {data,
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## [5840] {data,
## learn,
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## [5841] {dataset,
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## [5848] {data,
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## [5849] {data,
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## [5850] {data,
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## [5864] {data,
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## [5895] {featur,
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## [5897] {model,
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## [5898] {featur,
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## [5899] {featur,
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## [5902] {compon,
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## [5903] {dataset,
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## [5904] {compon,
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## [5905] {model,
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## [5906] {model,
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## [5907] {model,
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## [5908] {compon,
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## [5909] {model,
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## [5910] {model,
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## [5911] {model,
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## [5912] {compon,
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## [5913] {model,
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## [5914] {model,
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## [5916] {neural,
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## [5917] {train,
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## [5918] {train,
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## [5919] {train,
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## [5920] {dataset,
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## [5921] {train,
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## [5922] {train,
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## [5923] {network,
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## [5924] {network,
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## [5925] {neural,
## provid,
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## [5926] {dataset,
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## [5927] {dataset,
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## [5928] {dataset,
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## [5929] {neural,
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## [5930] {network,
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## [5931] {network,
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## [5932] {dataset,
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## [5933] {network,
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## [5934] {network,
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## [5935] {train,
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## [5936] {train,
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## [5937] {dataset,
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## [5938] {train,
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## [5939] {train,
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## [5940] {network,
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## [5941] {network,
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## [5942] {network,
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## [5943] {train,
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## [5944] {network,
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## [5945] {network,
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## [5946] {dataset,
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## [5947] {network,
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## [5948] {network,
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## [5949] {classif,
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## [5951] {classif,
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## [5952] {classif,
## object,
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## [5953] {classif,
## object,
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## [5954] {model,
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## [5955] {classif,
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## [5956] {classif,
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## [5957] {classif,
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## [5958] {featur,
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## [5959] {classif,
## featur,
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## [5960] {classif,
## featur,
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## [5962] {model,
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## [5964] {model,
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## [5965] {object,
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## [5966] {featur,
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## [5967] {featur,
## propos,
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## [5968] {model,
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## [5969] {featur,
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## [5970] {featur,
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## [5971] {featur,
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## [5973] {classif,
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## [5975] {classif,
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## [5976] {classif,
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## [5977] {classif,
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## [5978] {featur,
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## [5979] {classif,
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## [5980] {classif,
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## [5981] {featur,
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## [5982] {model,
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## [5983] {featur,
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## [5984] {featur,
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## [5986] {extract,
## general,
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## [5987] {extract,
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## [5988] {general,
## recognit,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [5989] {extract,
## general,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [5990] {show,
## extract,
## general} => {recognit} 0.1000000 1.0000000 3.333333 3
## [5991] {show,
## extract,
## recognit} => {general} 0.1000000 1.0000000 5.000000 3
## [5992] {show,
## general,
## recognit} => {extract} 0.1000000 0.7500000 5.625000 3
## [5993] {extract,
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## [5994] {featur,
## extract,
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## [5995] {featur,
## extract,
## recognit} => {general} 0.1000000 1.0000000 5.000000 3
## [5996] {featur,
## general,
## recognit} => {extract} 0.1000000 1.0000000 7.500000 3
## [5997] {extract,
## general,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [5998] {show,
## extract,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [5999] {show,
## extract,
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## [6000] {show,
## general,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6001] {extract,
## general,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [6002] {featur,
## extract,
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## [6003] {featur,
## extract,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [6004] {featur,
## general,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6005] {show,
## extract,
## general} => {featur} 0.1000000 1.0000000 1.875000 3
## [6006] {featur,
## extract,
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## [6007] {featur,
## show,
## extract} => {general} 0.1000000 1.0000000 5.000000 3
## [6008] {featur,
## show,
## general} => {extract} 0.1000000 1.0000000 7.500000 3
## [6009] {extract,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6010] {show,
## extract,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [6011] {show,
## extract,
## result} => {recognit} 0.1000000 0.7500000 2.500000 3
## [6012] {show,
## recognit,
## result} => {extract} 0.1000000 0.7500000 5.625000 3
## [6013] {extract,
## recognit,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [6014] {featur,
## extract,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [6015] {featur,
## extract,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6016] {featur,
## recognit,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6017] {show,
## extract,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [6018] {featur,
## extract,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [6019] {featur,
## show,
## extract} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6020] {featur,
## show,
## recognit} => {extract} 0.1000000 0.7500000 5.625000 3
## [6021] {algorithm,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6022] {show,
## extract,
## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6023] {show,
## algorithm,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6024] {show,
## algorithm,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6025] {algorithm,
## extract,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [6026] {model,
## extract,
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## [6027] {model,
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## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6028] {model,
## algorithm,
## result} => {extract} 0.1000000 0.7500000 5.625000 3
## [6029] {data,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6030] {show,
## extract,
## result} => {data} 0.1000000 0.7500000 1.730769 3
## [6031] {data,
## show,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6032] {data,
## show,
## result} => {extract} 0.1000000 0.7500000 5.625000 3
## [6033] {show,
## extract,
## result} => {model} 0.1000000 0.7500000 1.406250 3
## [6034] {model,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6035] {model,
## show,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6036] {model,
## show,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6037] {show,
## extract,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [6038] {featur,
## extract,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [6039] {featur,
## show,
## extract} => {result} 0.1000000 1.0000000 3.000000 3
## [6040] {featur,
## show,
## result} => {extract} 0.1000000 1.0000000 7.500000 3
## [6041] {show,
## algorithm,
## extract} => {model} 0.1000000 1.0000000 1.875000 3
## [6042] {model,
## algorithm,
## extract} => {show} 0.1000000 1.0000000 1.875000 3
## [6043] {model,
## show,
## extract} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6044] {appli,
## dataset,
## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [6045] {appli,
## propos,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6046] {dataset,
## propos,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [6047] {appli,
## dataset,
## propos} => {report} 0.1000000 0.7500000 5.625000 3
## [6048] {appli,
## dataset,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [6049] {network,
## appli,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6050] {network,
## dataset,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [6051] {network,
## appli,
## dataset} => {report} 0.1000000 0.7500000 5.625000 3
## [6052] {appli,
## propos,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [6053] {network,
## appli,
## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [6054] {network,
## propos,
## report} => {appli} 0.1000000 1.0000000 5.000000 3
## [6055] {network,
## appli,
## propos} => {report} 0.1000000 0.7500000 5.625000 3
## [6056] {dataset,
## propos,
## report} => {network} 0.1000000 1.0000000 1.578947 3
## [6057] {network,
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## report} => {propos} 0.1000000 1.0000000 2.000000 3
## [6058] {network,
## propos,
## report} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6059] {attribut,
## outperform,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [6060] {attribut,
## outperform,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [6061] {attribut,
## represent,
## show} => {outperform} 0.1000000 1.0000000 7.500000 3
## [6062] {outperform,
## represent,
## show} => {attribut} 0.1000000 1.0000000 10.000000 3
## [6063] {data,
## employ,
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## [6064] {employ,
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## [6065] {data,
## employ,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [6066] {data,
## employ,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6067] {employ,
## featur,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6068] {data,
## employ,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [6069] {employ,
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## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6070] {employ,
## featur,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6071] {employ,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [6072] {data,
## employ,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [6073] {data,
## employ,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [6074] {employ,
## featur,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [6075] {approach,
## achiev,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [6076] {achiev,
## propos,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [6077] {approach,
## propos,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [6078] {approach,
## achiev,
## propos} => {abil} 0.1000000 0.7500000 7.500000 3
## [6079] {approach,
## achiev,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [6080] {network,
## achiev,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [6081] {approach,
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## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [6082] {approach,
## network,
## achiev} => {abil} 0.1000000 0.7500000 7.500000 3
## [6083] {achiev,
## propos,
## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [6084] {network,
## achiev,
## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [6085] {network,
## propos,
## abil} => {achiev} 0.1000000 1.0000000 4.285714 3
## [6086] {network,
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## [6087] {approach,
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## abil} => {network} 0.1000000 1.0000000 1.578947 3
## [6088] {approach,
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## abil} => {propos} 0.1000000 1.0000000 2.000000 3
## [6089] {network,
## propos,
## abil} => {approach} 0.1000000 1.0000000 2.500000 3
## [6090] {data,
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## [6091] {show,
## algorithm,
## structur} => {data} 0.1000000 1.0000000 2.307692 3
## [6092] {data,
## show,
## structur} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6093] {data,
## show,
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## [6094] {data,
## algorithm,
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## [6095] {model,
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## [6096] {data,
## model,
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## [6097] {data,
## model,
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## [6098] {show,
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## [6099] {model,
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## [6100] {model,
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## [6101] {data,
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## [6102] {data,
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## [6103] {model,
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## [6104] {show,
## learn,
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## [6105] {featur,
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## [6106] {featur,
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## [6107] {show,
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## [6108] {propos,
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## [6109] {show,
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## [6110] {show,
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## [6111] {model,
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## [6112] {model,
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## [6113] {propos,
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## [6114] {model,
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## [6115] {model,
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## [6116] {show,
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## [6117] {model,
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## [6118] {model,
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## [6119] {model,
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## [6121] {convolut,
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## [6122] {architectur,
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## [6123] {architectur,
## effici,
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## [6124] {architectur,
## convolut,
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## [6125] {perform,
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## [6126] {architectur,
## perform,
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## [6127] {architectur,
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## [6128] {architectur,
## convolut,
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## [6129] {network,
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## [6130] {network,
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## [6131] {network,
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## [6132] {convolut,
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## [6133] {perform,
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## [6134] {perform,
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## [6135] {perform,
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## [6136] {convolut,
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## [6137] {network,
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## [6138] {network,
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## [6139] {network,
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## [6140] {perform,
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## [6141] {network,
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## [6142] {network,
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## [6143] {network,
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## [6144] {architectur,
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## [6145] {architectur,
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## [6146] {perform,
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## [6147] {architectur,
## perform,
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## [6148] {architectur,
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## [6149] {network,
## architectur,
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## [6150] {network,
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## [6151] {architectur,
## perform,
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## [6152] {network,
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## convolut} => {perform} 0.1000000 1.0000000 2.142857 3
## [6153] {network,
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## [6154] {perform,
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## [6155] {network,
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## [6156] {network,
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## [6157] {network,
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## [6158] {work,
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## [6159] {represent,
## larg,
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## [6160] {represent,
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## [6161] {represent,
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## [6175] {featur,
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## [6176] {featur,
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## [6300] {approach,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6301] {represent,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [6302] {approach,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [6303] {approach,
## featur,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6304] {featur,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [6305] {approach,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [6306] {approach,
## network,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6307] {network,
## captur,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [6308] {approach,
## network,
## learn} => {captur} 0.1000000 0.7500000 7.500000 3
## [6309] {approach,
## represent,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [6310] {approach,
## featur,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [6311] {featur,
## represent,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [6312] {approach,
## represent,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [6313] {approach,
## network,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [6314] {network,
## represent,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [6315] {approach,
## featur,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [6316] {approach,
## network,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [6317] {featur,
## network,
## captur} => {approach} 0.1000000 1.0000000 2.500000 3
## [6318] {represent,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [6319] {featur,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [6320] {featur,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6321] {represent,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [6322] {network,
## captur,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [6323] {network,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6324] {featur,
## captur,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [6325] {network,
## captur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [6326] {featur,
## network,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [6327] {featur,
## represent,
## captur} => {network} 0.1000000 1.0000000 1.578947 3
## [6328] {network,
## represent,
## captur} => {featur} 0.1000000 1.0000000 1.875000 3
## [6329] {featur,
## network,
## captur} => {represent} 0.1000000 1.0000000 2.000000 3
## [6330] {approach,
## dataset,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [6331] {approach,
## propos,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6332] {dataset,
## propos,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [6333] {approach,
## dataset,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [6334] {approach,
## network,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6335] {network,
## dataset,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [6336] {approach,
## propos,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [6337] {approach,
## network,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [6338] {network,
## propos,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [6339] {dataset,
## propos,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [6340] {network,
## dataset,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [6341] {network,
## propos,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6342] {process,
## simpl,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [6343] {simpl,
## studi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [6344] {process,
## simpl,
## work} => {studi} 0.1000000 1.0000000 7.500000 3
## [6345] {process,
## studi,
## work} => {simpl} 0.1000000 1.0000000 10.000000 3
## [6346] {process,
## simpl,
## studi} => {network} 0.1000000 1.0000000 1.578947 3
## [6347] {network,
## simpl,
## studi} => {process} 0.1000000 1.0000000 5.000000 3
## [6348] {network,
## process,
## simpl} => {studi} 0.1000000 1.0000000 7.500000 3
## [6349] {network,
## process,
## studi} => {simpl} 0.1000000 1.0000000 10.000000 3
## [6350] {simpl,
## studi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [6351] {network,
## simpl,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [6352] {network,
## simpl,
## work} => {studi} 0.1000000 1.0000000 7.500000 3
## [6353] {network,
## studi,
## work} => {simpl} 0.1000000 1.0000000 10.000000 3
## [6354] {process,
## simpl,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [6355] {network,
## process,
## simpl} => {work} 0.1000000 1.0000000 2.500000 3
## [6356] {network,
## simpl,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [6357] {network,
## process,
## work} => {simpl} 0.1000000 0.7500000 7.500000 3
## [6358] {train,
## result,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6359] {dataset,
## result,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [6360] {train,
## dataset,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [6361] {train,
## dataset,
## result} => {increas} 0.1000000 0.7500000 5.625000 3
## [6362] {train,
## result,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6363] {network,
## result,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [6364] {network,
## train,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [6365] {dataset,
## result,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6366] {network,
## result,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6367] {network,
## dataset,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [6368] {algorithm,
## neural,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6369] {network,
## neural,
## increas} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6370] {network,
## algorithm,
## increas} => {neural} 0.1000000 1.0000000 3.000000 3
## [6371] {train,
## dataset,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6372] {network,
## train,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6373] {network,
## dataset,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [6374] {featur,
## represent,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [6375] {network,
## represent,
## increas} => {featur} 0.1000000 1.0000000 1.875000 3
## [6376] {featur,
## network,
## increas} => {represent} 0.1000000 1.0000000 2.000000 3
## [6377] {boltzmann,
## restrict,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6378] {boltzmann,
## machin,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [6379] {boltzmann,
## machin,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6380] {machin,
## restrict,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6381] {boltzmann,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6382] {boltzmann,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6383] {boltzmann,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6384] {restrict,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6385] {boltzmann,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6386] {boltzmann,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6387] {boltzmann,
## show,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6388] {restrict,
## show,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6389] {boltzmann,
## restrict,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6390] {boltzmann,
## model,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [6391] {boltzmann,
## model,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6392] {model,
## restrict,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6393] {boltzmann,
## machin,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6394] {boltzmann,
## restrict,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6395] {boltzmann,
## machin,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6396] {machin,
## restrict,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6397] {boltzmann,
## machin,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [6398] {boltzmann,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6399] {boltzmann,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6400] {machin,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6401] {boltzmann,
## machin,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [6402] {boltzmann,
## restrict,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6403] {boltzmann,
## machin,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6404] {machin,
## restrict,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6405] {boltzmann,
## machin,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [6406] {boltzmann,
## data,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6407] {boltzmann,
## data,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6408] {data,
## machin,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6409] {boltzmann,
## machin,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [6410] {boltzmann,
## restrict,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [6411] {boltzmann,
## machin,
## show} => {restrict} 0.1333333 1.0000000 7.500000 4
## [6412] {machin,
## restrict,
## show} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [6413] {boltzmann,
## machin,
## restrict} => {model} 0.1333333 1.0000000 1.875000 4
## [6414] {boltzmann,
## model,
## restrict} => {machin} 0.1333333 1.0000000 4.285714 4
## [6415] {boltzmann,
## machin,
## model} => {restrict} 0.1333333 1.0000000 7.500000 4
## [6416] {machin,
## model,
## restrict} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [6417] {boltzmann,
## machin,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [6418] {boltzmann,
## featur,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6419] {boltzmann,
## featur,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6420] {featur,
## machin,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6421] {boltzmann,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6422] {boltzmann,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6423] {boltzmann,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6424] {restrict,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6425] {boltzmann,
## restrict,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6426] {boltzmann,
## model,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6427] {boltzmann,
## model,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6428] {model,
## restrict,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6429] {boltzmann,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6430] {boltzmann,
## data,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6431] {boltzmann,
## data,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6432] {data,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6433] {boltzmann,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6434] {boltzmann,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6435] {boltzmann,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6436] {restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6437] {boltzmann,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6438] {boltzmann,
## model,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [6439] {boltzmann,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6440] {model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6441] {boltzmann,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6442] {boltzmann,
## featur,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6443] {boltzmann,
## featur,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6444] {featur,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6445] {boltzmann,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6446] {boltzmann,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6447] {boltzmann,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6448] {restrict,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6449] {boltzmann,
## restrict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6450] {boltzmann,
## model,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [6451] {boltzmann,
## model,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6452] {model,
## restrict,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6453] {boltzmann,
## data,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6454] {boltzmann,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6455] {boltzmann,
## data,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6456] {data,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6457] {boltzmann,
## data,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6458] {boltzmann,
## model,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [6459] {boltzmann,
## data,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6460] {data,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6461] {boltzmann,
## data,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6462] {boltzmann,
## featur,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [6463] {boltzmann,
## data,
## featur} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6464] {data,
## featur,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6465] {boltzmann,
## restrict,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [6466] {boltzmann,
## model,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [6467] {boltzmann,
## model,
## show} => {restrict} 0.1333333 1.0000000 7.500000 4
## [6468] {model,
## restrict,
## show} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [6469] {boltzmann,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6470] {boltzmann,
## featur,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6471] {boltzmann,
## featur,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6472] {featur,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6473] {boltzmann,
## model,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [6474] {boltzmann,
## featur,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6475] {boltzmann,
## featur,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6476] {featur,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6477] {boltzmann,
## machin,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6478] {boltzmann,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6479] {boltzmann,
## machin,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6480] {machin,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6481] {boltzmann,
## machin,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6482] {boltzmann,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6483] {boltzmann,
## machin,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6484] {machin,
## show,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6485] {boltzmann,
## machin,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6486] {boltzmann,
## model,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6487] {boltzmann,
## machin,
## model} => {recent} 0.1000000 0.7500000 3.214286 3
## [6488] {machin,
## model,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6489] {boltzmann,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6490] {boltzmann,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6491] {boltzmann,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6492] {show,
## algorithm,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6493] {boltzmann,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6494] {boltzmann,
## model,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6495] {boltzmann,
## model,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6496] {model,
## algorithm,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6497] {boltzmann,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6498] {boltzmann,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6499] {boltzmann,
## model,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6500] {boltzmann,
## machin,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6501] {boltzmann,
## machin,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6502] {boltzmann,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6503] {machin,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6504] {boltzmann,
## machin,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6505] {boltzmann,
## machin,
## model} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6506] {boltzmann,
## model,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6507] {machin,
## model,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6508] {boltzmann,
## machin,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6509] {boltzmann,
## data,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [6510] {boltzmann,
## data,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6511] {data,
## machin,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6512] {boltzmann,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6513] {boltzmann,
## machin,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6514] {boltzmann,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6515] {machin,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6516] {boltzmann,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6517] {boltzmann,
## machin,
## model} => {task} 0.1000000 0.7500000 2.045455 3
## [6518] {boltzmann,
## model,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6519] {machin,
## model,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6520] {boltzmann,
## machin,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6521] {boltzmann,
## featur,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [6522] {boltzmann,
## featur,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6523] {featur,
## machin,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6524] {boltzmann,
## machin,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6525] {boltzmann,
## machin,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6526] {boltzmann,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6527] {machin,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6528] {boltzmann,
## machin,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6529] {boltzmann,
## machin,
## model} => {train} 0.1000000 0.7500000 1.875000 3
## [6530] {boltzmann,
## model,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6531] {machin,
## model,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6532] {boltzmann,
## data,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [6533] {boltzmann,
## machin,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6534] {boltzmann,
## data,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [6535] {data,
## machin,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6536] {boltzmann,
## data,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [6537] {boltzmann,
## machin,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [6538] {boltzmann,
## data,
## model} => {machin} 0.1000000 1.0000000 4.285714 3
## [6539] {data,
## machin,
## model} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6540] {boltzmann,
## data,
## machin} => {featur} 0.1000000 1.0000000 1.875000 3
## [6541] {boltzmann,
## featur,
## machin} => {data} 0.1000000 1.0000000 2.307692 3
## [6542] {boltzmann,
## data,
## featur} => {machin} 0.1000000 1.0000000 4.285714 3
## [6543] {data,
## featur,
## machin} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6544] {boltzmann,
## machin,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [6545] {boltzmann,
## machin,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [6546] {boltzmann,
## model,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [6547] {machin,
## model,
## show} => {boltzmann} 0.1333333 0.8000000 6.000000 4
## [6548] {boltzmann,
## machin,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6549] {boltzmann,
## featur,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [6550] {boltzmann,
## featur,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [6551] {featur,
## machin,
## show} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6552] {boltzmann,
## machin,
## model} => {featur} 0.1000000 0.7500000 1.406250 3
## [6553] {boltzmann,
## featur,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [6554] {boltzmann,
## featur,
## model} => {machin} 0.1000000 1.0000000 4.285714 3
## [6555] {featur,
## machin,
## model} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6556] {boltzmann,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6557] {boltzmann,
## model,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6558] {boltzmann,
## model,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6559] {boltzmann,
## data,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6560] {boltzmann,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6561] {boltzmann,
## data,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [6562] {boltzmann,
## data,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6563] {boltzmann,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6564] {boltzmann,
## data,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [6565] {boltzmann,
## data,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6566] {boltzmann,
## featur,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6567] {boltzmann,
## data,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [6568] {boltzmann,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6569] {boltzmann,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6570] {boltzmann,
## model,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6571] {model,
## show,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [6572] {boltzmann,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6573] {boltzmann,
## featur,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6574] {boltzmann,
## featur,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [6575] {boltzmann,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6576] {boltzmann,
## featur,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6577] {boltzmann,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [6578] {boltzmann,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6579] {boltzmann,
## model,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6580] {boltzmann,
## model,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6581] {model,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [6582] {boltzmann,
## data,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [6583] {boltzmann,
## data,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [6584] {boltzmann,
## model,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6585] {boltzmann,
## data,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [6586] {boltzmann,
## data,
## featur} => {show} 0.1000000 1.0000000 1.875000 3
## [6587] {boltzmann,
## featur,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [6588] {boltzmann,
## data,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [6589] {boltzmann,
## data,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [6590] {boltzmann,
## featur,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [6591] {boltzmann,
## model,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6592] {boltzmann,
## featur,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [6593] {boltzmann,
## featur,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [6594] {machin,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6595] {restrict,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6596] {machin,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6597] {machin,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6598] {machin,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6599] {restrict,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6600] {machin,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6601] {machin,
## show,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6602] {machin,
## restrict,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6603] {model,
## restrict,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [6604] {machin,
## model,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [6605] {machin,
## model,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6606] {restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6607] {restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6608] {restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6609] {show,
## algorithm,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6610] {restrict,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6611] {model,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6612] {model,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [6613] {model,
## algorithm,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6614] {restrict,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [6615] {model,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [6616] {model,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [6617] {machin,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6618] {machin,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6619] {restrict,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6620] {machin,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6621] {machin,
## restrict,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6622] {machin,
## model,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6623] {model,
## restrict,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [6624] {machin,
## model,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6625] {machin,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6626] {data,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6627] {data,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6628] {data,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6629] {machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6630] {machin,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6631] {restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6632] {machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6633] {machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6634] {machin,
## model,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [6635] {model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6636] {machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6637] {machin,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6638] {featur,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6639] {featur,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [6640] {featur,
## machin,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6641] {machin,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6642] {machin,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6643] {restrict,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6644] {machin,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6645] {machin,
## restrict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6646] {machin,
## model,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [6647] {model,
## restrict,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [6648] {machin,
## model,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6649] {data,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6650] {machin,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6651] {data,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [6652] {data,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6653] {data,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6654] {machin,
## model,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [6655] {data,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6656] {data,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6657] {data,
## machin,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6658] {featur,
## machin,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [6659] {data,
## featur,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6660] {data,
## featur,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6661] {machin,
## restrict,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [6662] {machin,
## model,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [6663] {model,
## restrict,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [6664] {machin,
## model,
## show} => {restrict} 0.1333333 0.8000000 6.000000 4
## [6665] {machin,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6666] {featur,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6667] {featur,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [6668] {featur,
## machin,
## show} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6669] {machin,
## model,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [6670] {featur,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6671] {featur,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [6672] {featur,
## machin,
## model} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6673] {restrict,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [6674] {model,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [6675] {model,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [6676] {data,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6677] {restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6678] {data,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [6679] {data,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6680] {model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6681] {data,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6682] {data,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6683] {featur,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [6684] {data,
## featur,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6685] {restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6686] {model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6687] {model,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [6688] {model,
## show,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [6689] {restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6690] {featur,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [6691] {featur,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [6692] {model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [6693] {featur,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [6694] {featur,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [6695] {restrict,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6696] {model,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6697] {model,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [6698] {model,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [6699] {data,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [6700] {data,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6701] {model,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [6702] {data,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [6703] {data,
## featur,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6704] {featur,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [6705] {data,
## model,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6706] {data,
## featur,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [6707] {featur,
## model,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [6708] {model,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [6709] {featur,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [6710] {featur,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [6711] {dataset,
## learn,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [6712] {dataset,
## propos,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [6713] {propos,
## learn,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6714] {dataset,
## learn,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [6715] {model,
## dataset,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [6716] {model,
## learn,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6717] {dataset,
## propos,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [6718] {model,
## dataset,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [6719] {model,
## propos,
## extens} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6720] {propos,
## learn,
## extens} => {model} 0.1000000 1.0000000 1.875000 3
## [6721] {model,
## learn,
## extens} => {propos} 0.1000000 1.0000000 2.000000 3
## [6722] {model,
## propos,
## extens} => {learn} 0.1000000 1.0000000 2.307692 3
## [6723] {algorithm,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6724] {approach,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6725] {approach,
## algorithm,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [6726] {approach,
## algorithm,
## neural} => {order} 0.1000000 0.7500000 5.625000 3
## [6727] {algorithm,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6728] {model,
## neural,
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## [6729] {model,
## algorithm,
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## [6730] {model,
## algorithm,
## neural} => {order} 0.1000000 1.0000000 7.500000 3
## [6731] {algorithm,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6732] {network,
## neural,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6733] {network,
## algorithm,
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## [6734] {approach,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6735] {model,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6736] {approach,
## model,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [6737] {approach,
## model,
## neural} => {order} 0.1000000 1.0000000 7.500000 3
## [6738] {approach,
## neural,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6739] {network,
## neural,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6740] {approach,
## network,
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## [6741] {model,
## neural,
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## [6742] {network,
## neural,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6743] {model,
## network,
## order} => {neural} 0.1000000 1.0000000 3.000000 3
## [6744] {model,
## network,
## neural} => {order} 0.1000000 0.7500000 5.625000 3
## [6745] {approach,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6746] {model,
## algorithm,
## order} => {approach} 0.1000000 0.7500000 1.875000 3
## [6747] {approach,
## model,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6748] {approach,
## model,
## algorithm} => {order} 0.1000000 1.0000000 7.500000 3
## [6749] {approach,
## algorithm,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6750] {network,
## algorithm,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6751] {approach,
## network,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6752] {approach,
## network,
## algorithm} => {order} 0.1000000 0.7500000 5.625000 3
## [6753] {show,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6754] {model,
## algorithm,
## order} => {show} 0.1000000 0.7500000 1.406250 3
## [6755] {model,
## show,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6756] {show,
## algorithm,
## order} => {featur} 0.1000000 1.0000000 1.875000 3
## [6757] {featur,
## algorithm,
## order} => {show} 0.1000000 1.0000000 1.875000 3
## [6758] {featur,
## show,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6759] {featur,
## show,
## algorithm} => {order} 0.1000000 0.7500000 5.625000 3
## [6760] {algorithm,
## order,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [6761] {model,
## algorithm,
## order} => {propos} 0.1000000 0.7500000 1.500000 3
## [6762] {model,
## order,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6763] {model,
## algorithm,
## propos} => {order} 0.1000000 1.0000000 7.500000 3
## [6764] {model,
## algorithm,
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## [6765] {featur,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6766] {featur,
## model,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6767] {featur,
## model,
## algorithm} => {order} 0.1000000 0.7500000 5.625000 3
## [6768] {model,
## algorithm,
## order} => {network} 0.1000000 0.7500000 1.184211 3
## [6769] {network,
## algorithm,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6770] {model,
## network,
## order} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6771] {model,
## network,
## algorithm} => {order} 0.1000000 1.0000000 7.500000 3
## [6772] {approach,
## model,
## order} => {network} 0.1000000 1.0000000 1.578947 3
## [6773] {approach,
## network,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6774] {model,
## network,
## order} => {approach} 0.1000000 1.0000000 2.500000 3
## [6775] {model,
## show,
## order} => {featur} 0.1000000 1.0000000 1.875000 3
## [6776] {featur,
## show,
## order} => {model} 0.1000000 1.0000000 1.875000 3
## [6777] {featur,
## model,
## order} => {show} 0.1000000 1.0000000 1.875000 3
## [6778] {algorithm,
## power,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [6779] {train,
## power,
## specif} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [6780] {train,
## algorithm,
## power} => {specif} 0.1000000 1.0000000 6.000000 3
## [6781] {train,
## algorithm,
## specif} => {power} 0.1000000 0.7500000 5.625000 3
## [6782] {dataset,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6783] {demonstr,
## learn,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6784] {dataset,
## learn,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6785] {dataset,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6786] {dataset,
## demonstr,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6787] {model,
## demonstr,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6788] {model,
## dataset,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6789] {model,
## dataset,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6790] {dataset,
## demonstr,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6791] {featur,
## demonstr,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6792] {featur,
## dataset,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6793] {featur,
## dataset,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6794] {demonstr,
## learn,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6795] {model,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6796] {model,
## learn,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6797] {model,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6798] {demonstr,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6799] {featur,
## demonstr,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6800] {featur,
## learn,
## benchmark} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [6801] {featur,
## demonstr,
## learn} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6802] {model,
## demonstr,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6803] {featur,
## demonstr,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6804] {featur,
## model,
## benchmark} => {demonstr} 0.1000000 1.0000000 4.285714 3
## [6805] {featur,
## model,
## demonstr} => {benchmark} 0.1000000 1.0000000 7.500000 3
## [6806] {classif,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6807] {classif,
## featur,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6808] {featur,
## learn,
## benchmark} => {classif} 0.1000000 0.7500000 2.812500 3
## [6809] {perform,
## problem,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6810] {problem,
## learn,
## benchmark} => {perform} 0.1000000 1.0000000 2.142857 3
## [6811] {perform,
## learn,
## benchmark} => {problem} 0.1000000 1.0000000 3.333333 3
## [6812] {perform,
## problem,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6813] {featur,
## problem,
## benchmark} => {perform} 0.1000000 1.0000000 2.142857 3
## [6814] {featur,
## perform,
## benchmark} => {problem} 0.1000000 1.0000000 3.333333 3
## [6815] {problem,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6816] {featur,
## problem,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6817] {featur,
## learn,
## benchmark} => {problem} 0.1000000 0.7500000 2.500000 3
## [6818] {featur,
## problem,
## learn} => {benchmark} 0.1000000 0.7500000 5.625000 3
## [6819] {perform,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6820] {featur,
## perform,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6821] {featur,
## learn,
## benchmark} => {perform} 0.1000000 0.7500000 1.607143 3
## [6822] {dataset,
## learn,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6823] {model,
## dataset,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6824] {model,
## learn,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6825] {dataset,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6826] {featur,
## dataset,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6827] {featur,
## learn,
## benchmark} => {dataset} 0.1000000 0.7500000 1.730769 3
## [6828] {model,
## dataset,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6829] {featur,
## dataset,
## benchmark} => {model} 0.1000000 1.0000000 1.875000 3
## [6830] {featur,
## model,
## benchmark} => {dataset} 0.1000000 1.0000000 2.307692 3
## [6831] {propos,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6832] {featur,
## learn,
## benchmark} => {propos} 0.1000000 0.7500000 1.500000 3
## [6833] {featur,
## propos,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6834] {model,
## learn,
## benchmark} => {featur} 0.1000000 1.0000000 1.875000 3
## [6835] {featur,
## learn,
## benchmark} => {model} 0.1000000 0.7500000 1.406250 3
## [6836] {featur,
## model,
## benchmark} => {learn} 0.1000000 1.0000000 2.307692 3
## [6837] {represent,
## success,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6838] {propos,
## success,
## high} => {represent} 0.1000000 1.0000000 2.000000 3
## [6839] {represent,
## propos,
## high} => {success} 0.1000000 1.0000000 3.750000 3
## [6840] {represent,
## success,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6841] {featur,
## success,
## high} => {represent} 0.1000000 1.0000000 2.000000 3
## [6842] {featur,
## represent,
## high} => {success} 0.1000000 1.0000000 3.750000 3
## [6843] {propos,
## success,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6844] {featur,
## success,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6845] {featur,
## propos,
## high} => {success} 0.1000000 0.7500000 2.812500 3
## [6846] {featur,
## propos,
## success} => {high} 0.1000000 0.7500000 5.625000 3
## [6847] {object,
## propos,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6848] {featur,
## object,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6849] {featur,
## propos,
## high} => {object} 0.1000000 0.7500000 2.812500 3
## [6850] {classif,
## learn,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6851] {classif,
## propos,
## high} => {learn} 0.1000000 1.0000000 2.307692 3
## [6852] {propos,
## learn,
## high} => {classif} 0.1000000 1.0000000 3.750000 3
## [6853] {classif,
## propos,
## learn} => {high} 0.1000000 0.7500000 5.625000 3
## [6854] {classif,
## learn,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6855] {classif,
## featur,
## high} => {learn} 0.1000000 1.0000000 2.307692 3
## [6856] {featur,
## learn,
## high} => {classif} 0.1000000 1.0000000 3.750000 3
## [6857] {classif,
## propos,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6858] {classif,
## featur,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6859] {featur,
## propos,
## high} => {classif} 0.1000000 0.7500000 2.812500 3
## [6860] {task,
## propos,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6861] {featur,
## task,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6862] {featur,
## propos,
## high} => {task} 0.1000000 0.7500000 2.045455 3
## [6863] {propos,
## learn,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6864] {featur,
## learn,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6865] {featur,
## propos,
## high} => {learn} 0.1000000 0.7500000 1.730769 3
## [6866] {represent,
## propos,
## high} => {featur} 0.1000000 1.0000000 1.875000 3
## [6867] {featur,
## represent,
## high} => {propos} 0.1000000 1.0000000 2.000000 3
## [6868] {featur,
## propos,
## high} => {represent} 0.1000000 0.7500000 1.500000 3
## [6869] {reduc,
## comput,
## parallel} => {network} 0.1000000 1.0000000 1.578947 3
## [6870] {network,
## comput,
## parallel} => {reduc} 0.1000000 1.0000000 4.285714 3
## [6871] {network,
## reduc,
## parallel} => {comput} 0.1000000 1.0000000 4.285714 3
## [6872] {network,
## reduc,
## comput} => {parallel} 0.1000000 1.0000000 7.500000 3
## [6873] {outperform,
## predict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [6874] {outperform,
## predict,
## show} => {train} 0.1000000 1.0000000 2.500000 3
## [6875] {predict,
## show,
## train} => {outperform} 0.1000000 1.0000000 7.500000 3
## [6876] {outperform,
## show,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [6877] {predict,
## train,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [6878] {predict,
## perform,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [6879] {predict,
## train,
## perform} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6880] {train,
## perform,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [6881] {predict,
## train,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [6882] {predict,
## propos,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [6883] {predict,
## train,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6884] {train,
## propos,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [6885] {predict,
## perform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [6886] {predict,
## propos,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [6887] {predict,
## perform,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6888] {perform,
## propos,
## challeng} => {predict} 0.1000000 1.0000000 7.500000 3
## [6889] {paper,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [6890] {data,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6891] {data,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [6892] {data,
## paper,
## train} => {predict} 0.1000000 0.7500000 5.625000 3
## [6893] {paper,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6894] {model,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6895] {model,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [6896] {model,
## paper,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [6897] {paper,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [6898] {featur,
## paper,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6899] {featur,
## predict,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [6900] {data,
## paper,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [6901] {model,
## paper,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [6902] {data,
## model,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [6903] {data,
## model,
## paper} => {predict} 0.1000000 0.7500000 5.625000 3
## [6904] {data,
## paper,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6905] {featur,
## paper,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [6906] {data,
## featur,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [6907] {data,
## featur,
## paper} => {predict} 0.1000000 0.7500000 5.625000 3
## [6908] {model,
## paper,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6909] {featur,
## paper,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [6910] {featur,
## model,
## predict} => {paper} 0.1000000 1.0000000 3.000000 3
## [6911] {featur,
## model,
## paper} => {predict} 0.1000000 1.0000000 7.500000 3
## [6912] {predict,
## train,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [6913] {predict,
## train,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [6914] {predict,
## perform,
## propos} => {train} 0.1000000 1.0000000 2.500000 3
## [6915] {train,
## perform,
## propos} => {predict} 0.1000000 0.7500000 5.625000 3
## [6916] {data,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6917] {model,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [6918] {data,
## model,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6919] {data,
## model,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [6920] {data,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [6921] {featur,
## predict,
## train} => {data} 0.1000000 1.0000000 2.307692 3
## [6922] {data,
## featur,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6923] {model,
## predict,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [6924] {featur,
## predict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [6925] {featur,
## model,
## predict} => {train} 0.1000000 1.0000000 2.500000 3
## [6926] {featur,
## model,
## train} => {predict} 0.1000000 1.0000000 7.500000 3
## [6927] {data,
## model,
## predict} => {featur} 0.1000000 1.0000000 1.875000 3
## [6928] {data,
## featur,
## predict} => {model} 0.1000000 1.0000000 1.875000 3
## [6929] {featur,
## model,
## predict} => {data} 0.1000000 1.0000000 2.307692 3
## [6930] {data,
## make,
## number} => {model} 0.1000000 1.0000000 1.875000 3
## [6931] {make,
## model,
## number} => {data} 0.1000000 1.0000000 2.307692 3
## [6932] {data,
## model,
## number} => {make} 0.1000000 1.0000000 3.333333 3
## [6933] {data,
## make,
## model} => {number} 0.1000000 0.7500000 4.500000 3
## [6934] {recognit,
## challeng,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [6935] {train,
## challeng,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6936] {train,
## recognit,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6937] {train,
## recognit,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [6938] {recognit,
## challeng,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [6939] {represent,
## challeng,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6940] {represent,
## recognit,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6941] {represent,
## recognit,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [6942] {train,
## challeng,
## face} => {represent} 0.1000000 1.0000000 2.000000 3
## [6943] {represent,
## challeng,
## face} => {train} 0.1000000 1.0000000 2.500000 3
## [6944] {represent,
## train,
## face} => {challeng} 0.1000000 1.0000000 6.000000 3
## [6945] {represent,
## train,
## challeng} => {face} 0.1000000 1.0000000 7.500000 3
## [6946] {recognit,
## imag,
## face} => {propos} 0.1000000 1.0000000 2.000000 3
## [6947] {propos,
## imag,
## face} => {recognit} 0.1000000 1.0000000 3.333333 3
## [6948] {propos,
## recognit,
## face} => {imag} 0.1000000 1.0000000 6.000000 3
## [6949] {propos,
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## [6953] {represent,
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## [6954] {data,
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## [6955] {dataset,
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## [6957] {data,
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## [6964] {data,
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## [6966] {recognit,
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## [6970] {featur,
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## [6974] {featur,
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## [6975] {featur,
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## [6977] {data,
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## [6978] {dataset,
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## [6980] {data,
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## [7025] {dataset,
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## [7026] {approach,
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## [7608] {show,
## general,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [7609] {show,
## joint,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [7610] {joint,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7611] {show,
## joint,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [7612] {approach,
## method,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7613] {method,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7614] {approach,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7615] {approach,
## method,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7616] {method,
## show,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7617] {approach,
## show,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7618] {approach,
## method,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7619] {method,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [7620] {approach,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [7621] {approach,
## method,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7622] {method,
## model,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7623] {approach,
## model,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7624] {method,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7625] {method,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7626] {show,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7627] {method,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7628] {method,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7629] {dataset,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [7630] {method,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7631] {method,
## model,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7632] {model,
## dataset,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7633] {method,
## model,
## dataset} => {joint} 0.1000000 0.7500000 5.625000 3
## [7634] {method,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7635] {method,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7636] {show,
## joint,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [7637] {method,
## show,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7638] {method,
## model,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7639] {model,
## show,
## joint} => {method} 0.1000000 1.0000000 2.727273 3
## [7640] {method,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [7641] {method,
## model,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7642] {model,
## joint,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [7643] {method,
## model,
## propos} => {joint} 0.1000000 1.0000000 7.500000 3
## [7644] {approach,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7645] {approach,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7646] {show,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7647] {approach,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7648] {approach,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7649] {dataset,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [7650] {approach,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7651] {approach,
## model,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7652] {model,
## dataset,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7653] {approach,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7654] {approach,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7655] {show,
## joint,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [7656] {approach,
## show,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7657] {approach,
## model,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7658] {model,
## show,
## joint} => {approach} 0.1000000 1.0000000 2.500000 3
## [7659] {approach,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [7660] {approach,
## model,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7661] {model,
## joint,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [7662] {show,
## joint,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [7663] {joint,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7664] {show,
## joint,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [7665] {show,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7666] {dataset,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7667] {show,
## joint,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [7668] {show,
## dataset,
## joint} => {model} 0.1000000 1.0000000 1.875000 3
## [7669] {model,
## dataset,
## joint} => {show} 0.1000000 1.0000000 1.875000 3
## [7670] {model,
## show,
## joint} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7671] {model,
## show,
## dataset} => {joint} 0.1000000 0.7500000 5.625000 3
## [7672] {dataset,
## joint,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [7673] {model,
## dataset,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7674] {model,
## joint,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7675] {represent,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7676] {represent,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7677] {show,
## joint,
## propos} => {represent} 0.1000000 0.7500000 1.500000 3
## [7678] {show,
## joint,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [7679] {model,
## show,
## joint} => {propos} 0.1000000 1.0000000 2.000000 3
## [7680] {model,
## joint,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7681] {model,
## show,
## propos} => {joint} 0.1000000 0.7500000 5.625000 3
## [7682] {addit,
## imag,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [7683] {classif,
## addit,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [7684] {classif,
## imag,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [7685] {classif,
## addit,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [7686] {addit,
## imag,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [7687] {propos,
## addit,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [7688] {propos,
## imag,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [7689] {propos,
## addit,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [7690] {classif,
## addit,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [7691] {propos,
## addit,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [7692] {classif,
## propos,
## studi} => {addit} 0.1000000 1.0000000 6.000000 3
## [7693] {classif,
## propos,
## addit} => {studi} 0.1000000 1.0000000 7.500000 3
## [7694] {classif,
## imag,
## studi} => {propos} 0.1000000 1.0000000 2.000000 3
## [7695] {propos,
## imag,
## studi} => {classif} 0.1000000 1.0000000 3.750000 3
## [7696] {classif,
## propos,
## studi} => {imag} 0.1000000 1.0000000 6.000000 3
## [7697] {classif,
## propos,
## imag} => {studi} 0.1000000 1.0000000 7.500000 3
## [7698] {process,
## studi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [7699] {network,
## process,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [7700] {network,
## studi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [7701] {network,
## process,
## work} => {studi} 0.1000000 0.7500000 5.625000 3
## [7702] {evalu,
## make,
## paper} => {method} 0.1000000 1.0000000 2.727273 3
## [7703] {evalu,
## make,
## method} => {paper} 0.1000000 1.0000000 3.000000 3
## [7704] {evalu,
## method,
## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [7705] {make,
## method,
## paper} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7706] {evalu,
## paper,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7707] {evalu,
## network,
## paper} => {represent} 0.1000000 1.0000000 2.000000 3
## [7708] {evalu,
## network,
## represent} => {paper} 0.1000000 0.7500000 2.250000 3
## [7709] {network,
## paper,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7710] {evalu,
## paper,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7711] {evalu,
## model,
## paper} => {show} 0.1000000 1.0000000 1.875000 3
## [7712] {evalu,
## model,
## show} => {paper} 0.1000000 0.7500000 2.250000 3
## [7713] {evalu,
## method,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [7714] {approach,
## evalu,
## method} => {task} 0.1000000 1.0000000 2.727273 3
## [7715] {approach,
## evalu,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [7716] {approach,
## method,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7717] {evalu,
## method,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7718] {evalu,
## method,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [7719] {evalu,
## represent,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [7720] {method,
## represent,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7721] {evalu,
## method,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7722] {evalu,
## featur,
## method} => {task} 0.1000000 1.0000000 2.727273 3
## [7723] {evalu,
## featur,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [7724] {featur,
## method,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7725] {evalu,
## method,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [7726] {evalu,
## method,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [7727] {evalu,
## network,
## task} => {method} 0.1000000 1.0000000 2.727273 3
## [7728] {method,
## network,
## task} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7729] {approach,
## evalu,
## method} => {represent} 0.1000000 1.0000000 2.000000 3
## [7730] {evalu,
## method,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [7731] {approach,
## evalu,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [7732] {approach,
## method,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7733] {approach,
## evalu,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [7734] {evalu,
## featur,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [7735] {approach,
## evalu,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [7736] {approach,
## evalu,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [7737] {evalu,
## method,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [7738] {approach,
## evalu,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [7739] {data,
## evalu,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [7740] {evalu,
## method,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7741] {data,
## evalu,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [7742] {data,
## method,
## show} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7743] {data,
## evalu,
## method} => {model} 0.1000000 1.0000000 1.875000 3
## [7744] {evalu,
## method,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [7745] {data,
## evalu,
## model} => {method} 0.1000000 1.0000000 2.727273 3
## [7746] {data,
## method,
## model} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7747] {evalu,
## method,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [7748] {evalu,
## featur,
## method} => {represent} 0.1000000 1.0000000 2.000000 3
## [7749] {evalu,
## featur,
## represent} => {method} 0.1000000 1.0000000 2.727273 3
## [7750] {featur,
## method,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7751] {evalu,
## method,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7752] {evalu,
## method,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [7753] {evalu,
## network,
## represent} => {method} 0.1000000 0.7500000 2.045455 3
## [7754] {method,
## network,
## represent} => {evalu} 0.1000000 1.0000000 6.000000 3
## [7755] {evalu,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7756] {evalu,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [7757] {evalu,
## model,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [7758] {evalu,
## featur,
## method} => {network} 0.1000000 1.0000000 1.578947 3
## [7759] {evalu,
## method,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [7760] {evalu,
## featur,
## network} => {method} 0.1000000 1.0000000 2.727273 3
## [7761] {approach,
## evalu,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7762] {evalu,
## represent,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [7763] {approach,
## evalu,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [7764] {approach,
## evalu,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7765] {evalu,
## featur,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [7766] {approach,
## evalu,
## featur} => {task} 0.1000000 1.0000000 2.727273 3
## [7767] {approach,
## evalu,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [7768] {evalu,
## network,
## task} => {approach} 0.1000000 1.0000000 2.500000 3
## [7769] {approach,
## evalu,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [7770] {approach,
## network,
## task} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7771] {evalu,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7772] {evalu,
## featur,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7773] {evalu,
## featur,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [7774] {evalu,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [7775] {evalu,
## network,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [7776] {evalu,
## network,
## represent} => {task} 0.1000000 0.7500000 2.045455 3
## [7777] {evalu,
## featur,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [7778] {evalu,
## network,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7779] {evalu,
## featur,
## network} => {task} 0.1000000 1.0000000 2.727273 3
## [7780] {approach,
## evalu,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [7781] {approach,
## evalu,
## featur} => {represent} 0.1000000 1.0000000 2.000000 3
## [7782] {evalu,
## featur,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [7783] {approach,
## evalu,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7784] {approach,
## evalu,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [7785] {evalu,
## network,
## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [7786] {approach,
## evalu,
## featur} => {network} 0.1000000 1.0000000 1.578947 3
## [7787] {approach,
## evalu,
## network} => {featur} 0.1000000 1.0000000 1.875000 3
## [7788] {evalu,
## featur,
## network} => {approach} 0.1000000 1.0000000 2.500000 3
## [7789] {data,
## evalu,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7790] {data,
## evalu,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [7791] {evalu,
## model,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [7792] {evalu,
## represent,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7793] {evalu,
## model,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [7794] {evalu,
## model,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [7795] {evalu,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [7796] {evalu,
## network,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [7797] {evalu,
## network,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [7798] {evalu,
## model,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7799] {evalu,
## network,
## represent} => {model} 0.1000000 0.7500000 1.406250 3
## [7800] {evalu,
## model,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [7801] {model,
## network,
## represent} => {evalu} 0.1000000 0.7500000 4.500000 3
## [7802] {evalu,
## featur,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [7803] {evalu,
## network,
## represent} => {featur} 0.1000000 0.7500000 1.406250 3
## [7804] {evalu,
## featur,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [7805] {evalu,
## model,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [7806] {evalu,
## network,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7807] {evalu,
## model,
## network} => {show} 0.1000000 1.0000000 1.875000 3
## [7808] {outperform,
## object,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [7809] {outperform,
## show,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [7810] {outperform,
## show,
## object} => {challeng} 0.1000000 1.0000000 6.000000 3
## [7811] {show,
## object,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [7812] {outperform,
## object,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [7813] {outperform,
## propos,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [7814] {outperform,
## object,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [7815] {object,
## propos,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [7816] {outperform,
## show,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [7817] {outperform,
## propos,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [7818] {outperform,
## show,
## propos} => {challeng} 0.1000000 1.0000000 6.000000 3
## [7819] {show,
## propos,
## challeng} => {outperform} 0.1000000 1.0000000 7.500000 3
## [7820] {outperform,
## show,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [7821] {outperform,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [7822] {outperform,
## show,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [7823] {data,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [7824] {outperform,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [7825] {data,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7826] {data,
## outperform,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [7827] {model,
## outperform,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [7828] {data,
## model,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [7829] {data,
## outperform,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7830] {featur,
## outperform,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [7831] {data,
## featur,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [7832] {outperform,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [7833] {model,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [7834] {model,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7835] {model,
## show,
## task} => {outperform} 0.1000000 0.7500000 5.625000 3
## [7836] {outperform,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7837] {featur,
## outperform,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [7838] {featur,
## outperform,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [7839] {model,
## outperform,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [7840] {featur,
## outperform,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [7841] {featur,
## model,
## outperform} => {task} 0.1000000 1.0000000 2.727273 3
## [7842] {data,
## outperform,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7843] {data,
## model,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [7844] {model,
## outperform,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7845] {data,
## outperform,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [7846] {data,
## featur,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [7847] {featur,
## outperform,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [7848] {data,
## model,
## outperform} => {featur} 0.1000000 1.0000000 1.875000 3
## [7849] {data,
## featur,
## outperform} => {model} 0.1000000 1.0000000 1.875000 3
## [7850] {featur,
## model,
## outperform} => {data} 0.1000000 1.0000000 2.307692 3
## [7851] {model,
## outperform,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [7852] {featur,
## outperform,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [7853] {featur,
## model,
## outperform} => {show} 0.1000000 1.0000000 1.875000 3
## [7854] {data,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7855] {task,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7856] {data,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7857] {data,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7858] {task,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7859] {data,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7860] {data,
## task,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7861] {featur,
## task,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7862] {data,
## featur,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7863] {data,
## task,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7864] {network,
## task,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7865] {data,
## network,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7866] {task,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7867] {task,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7868] {dataset,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7869] {task,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7870] {featur,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7871] {featur,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7872] {featur,
## task,
## dataset} => {design} 0.1000000 0.7500000 5.625000 3
## [7873] {task,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7874] {network,
## task,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7875] {network,
## dataset,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7876] {network,
## task,
## dataset} => {design} 0.1000000 0.7500000 5.625000 3
## [7877] {task,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7878] {featur,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7879] {featur,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7880] {task,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7881] {network,
## task,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7882] {network,
## learn,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7883] {featur,
## task,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7884] {network,
## task,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7885] {featur,
## network,
## design} => {task} 0.1000000 1.0000000 2.727273 3
## [7886] {data,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7887] {data,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7888] {dataset,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7889] {data,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7890] {data,
## featur,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7891] {featur,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7892] {data,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7893] {data,
## network,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7894] {network,
## dataset,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7895] {data,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7896] {data,
## featur,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7897] {featur,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7898] {data,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7899] {data,
## network,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7900] {network,
## learn,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7901] {data,
## network,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [7902] {data,
## featur,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7903] {data,
## network,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7904] {featur,
## network,
## design} => {data} 0.1000000 1.0000000 2.307692 3
## [7905] {dataset,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7906] {featur,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7907] {featur,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7908] {dataset,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7909] {network,
## dataset,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7910] {network,
## learn,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7911] {network,
## dataset,
## learn} => {design} 0.1000000 0.7500000 5.625000 3
## [7912] {featur,
## dataset,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7913] {network,
## dataset,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7914] {featur,
## network,
## design} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7915] {featur,
## learn,
## design} => {network} 0.1000000 1.0000000 1.578947 3
## [7916] {network,
## learn,
## design} => {featur} 0.1000000 1.0000000 1.875000 3
## [7917] {featur,
## network,
## design} => {learn} 0.1000000 1.0000000 2.307692 3
## [7918] {show,
## general,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [7919] {model,
## general,
## system} => {show} 0.1000000 1.0000000 1.875000 3
## [7920] {model,
## show,
## system} => {general} 0.1000000 1.0000000 5.000000 3
## [7921] {model,
## show,
## general} => {system} 0.1000000 1.0000000 6.000000 3
## [7922] {model,
## recognit,
## system} => {featur} 0.1000000 1.0000000 1.875000 3
## [7923] {featur,
## recognit,
## system} => {model} 0.1000000 1.0000000 1.875000 3
## [7924] {featur,
## model,
## system} => {recognit} 0.1000000 1.0000000 3.333333 3
## [7925] {featur,
## model,
## recognit} => {system} 0.1000000 0.7500000 4.500000 3
## [7926] {method,
## perform,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7927] {method,
## dataset,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7928] {dataset,
## perform,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [7929] {method,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7930] {method,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7931] {perform,
## propos,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [7932] {method,
## dataset,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7933] {method,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7934] {dataset,
## propos,
## system} => {method} 0.1000000 0.7500000 2.045455 3
## [7935] {dataset,
## perform,
## system} => {propos} 0.1333333 1.0000000 2.000000 4
## [7936] {perform,
## propos,
## system} => {dataset} 0.1333333 1.0000000 2.307692 4
## [7937] {dataset,
## propos,
## system} => {perform} 0.1333333 1.0000000 2.142857 4
## [7938] {dataset,
## perform,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [7939] {model,
## perform,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7940] {model,
## dataset,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7941] {dataset,
## perform,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [7942] {featur,
## perform,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7943] {featur,
## dataset,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7944] {perform,
## propos,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [7945] {model,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7946] {model,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7947] {perform,
## propos,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [7948] {featur,
## perform,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7949] {featur,
## propos,
## system} => {perform} 0.1000000 1.0000000 2.142857 3
## [7950] {dataset,
## propos,
## system} => {model} 0.1000000 0.7500000 1.406250 3
## [7951] {model,
## dataset,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7952] {model,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7953] {dataset,
## propos,
## system} => {featur} 0.1000000 0.7500000 1.406250 3
## [7954] {featur,
## dataset,
## system} => {propos} 0.1000000 1.0000000 2.000000 3
## [7955] {featur,
## propos,
## system} => {dataset} 0.1000000 1.0000000 2.307692 3
## [7956] {represent,
## system,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [7957] {model,
## system,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [7958] {model,
## represent,
## system} => {learn} 0.1000000 1.0000000 2.307692 3
## [7959] {method,
## detect,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [7960] {detect,
## propos,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [7961] {method,
## detect,
## propos} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [7962] {method,
## propos,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [7963] {method,
## detect,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [7964] {featur,
## detect,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [7965] {featur,
## method,
## detect} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [7966] {featur,
## method,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [7967] {detect,
## propos,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [7968] {featur,
## detect,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [7969] {featur,
## detect,
## propos} => {stateoftheart} 0.1000000 0.7500000 4.500000 3
## [7970] {featur,
## propos,
## stateoftheart} => {detect} 0.1000000 1.0000000 6.000000 3
## [7971] {method,
## appli,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [7972] {appli,
## detect,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [7973] {method,
## detect,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [7974] {method,
## appli,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [7975] {method,
## appli,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [7976] {appli,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [7977] {method,
## detect,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [7978] {method,
## appli,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [7979] {method,
## appli,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [7980] {featur,
## appli,
## detect} => {method} 0.1000000 1.0000000 2.727273 3
## [7981] {featur,
## method,
## detect} => {appli} 0.1000000 0.7500000 3.750000 3
## [7982] {featur,
## method,
## appli} => {detect} 0.1000000 1.0000000 6.000000 3
## [7983] {appli,
## detect,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [7984] {appli,
## detect,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [7985] {detect,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [7986] {appli,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [7987] {featur,
## appli,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [7988] {featur,
## detect,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [7989] {featur,
## appli,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [7990] {appli,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [7991] {featur,
## appli,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [7992] {featur,
## detect,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [7993] {featur,
## appli,
## propos} => {detect} 0.1000000 0.7500000 4.500000 3
## [7994] {method,
## detect,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [7995] {show,
## detect,
## object} => {method} 0.1000000 1.0000000 2.727273 3
## [7996] {method,
## show,
## detect} => {object} 0.1000000 1.0000000 3.750000 3
## [7997] {method,
## show,
## object} => {detect} 0.1000000 0.7500000 4.500000 3
## [7998] {method,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [7999] {detect,
## object,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8000] {method,
## detect,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [8001] {method,
## object,
## propos} => {detect} 0.1000000 1.0000000 6.000000 3
## [8002] {method,
## detect,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [8003] {featur,
## detect,
## object} => {method} 0.1000000 1.0000000 2.727273 3
## [8004] {featur,
## method,
## detect} => {object} 0.1000000 0.7500000 2.812500 3
## [8005] {featur,
## method,
## object} => {detect} 0.1000000 1.0000000 6.000000 3
## [8006] {show,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [8007] {detect,
## object,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [8008] {show,
## detect,
## propos} => {object} 0.1000000 1.0000000 3.750000 3
## [8009] {show,
## detect,
## object} => {featur} 0.1000000 1.0000000 1.875000 3
## [8010] {featur,
## detect,
## object} => {show} 0.1000000 1.0000000 1.875000 3
## [8011] {featur,
## show,
## detect} => {object} 0.1000000 1.0000000 3.750000 3
## [8012] {featur,
## show,
## object} => {detect} 0.1000000 0.7500000 4.500000 3
## [8013] {detect,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [8014] {featur,
## detect,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [8015] {featur,
## detect,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [8016] {architectur,
## detect,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8017] {architectur,
## dataset,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [8018] {dataset,
## detect,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8019] {architectur,
## dataset,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [8020] {architectur,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [8021] {featur,
## architectur,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [8022] {featur,
## detect,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8023] {featur,
## architectur,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [8024] {architectur,
## detect,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [8025] {network,
## architectur,
## detect} => {perform} 0.1000000 1.0000000 2.142857 3
## [8026] {network,
## detect,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8027] {architectur,
## dataset,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8028] {featur,
## architectur,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8029] {featur,
## dataset,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8030] {featur,
## architectur,
## dataset} => {detect} 0.1000000 0.7500000 4.500000 3
## [8031] {architectur,
## dataset,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [8032] {network,
## architectur,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8033] {network,
## dataset,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8034] {featur,
## architectur,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [8035] {network,
## architectur,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8036] {featur,
## network,
## detect} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8037] {method,
## detect,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [8038] {method,
## detect,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [8039] {detect,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8040] {method,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [8041] {featur,
## method,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [8042] {featur,
## detect,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [8043] {featur,
## method,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [8044] {method,
## dataset,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [8045] {method,
## detect,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8046] {dataset,
## detect,
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## [8047] {method,
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## [8048] {featur,
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## [8049] {featur,
## dataset,
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## [8050] {featur,
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## [8051] {method,
## dataset,
## detect} => {network} 0.1000000 1.0000000 1.578947 3
## [8052] {method,
## network,
## detect} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8053] {network,
## dataset,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [8054] {method,
## network,
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## [8055] {method,
## show,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [8056] {method,
## detect,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [8057] {show,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8058] {method,
## show,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8059] {featur,
## method,
## detect} => {show} 0.1000000 0.7500000 1.406250 3
## [8060] {featur,
## show,
## detect} => {method} 0.1000000 1.0000000 2.727273 3
## [8061] {method,
## detect,
## propos} => {featur} 0.1333333 1.0000000 1.875000 4
## [8062] {featur,
## method,
## detect} => {propos} 0.1333333 1.0000000 2.000000 4
## [8063] {featur,
## detect,
## propos} => {method} 0.1333333 1.0000000 2.727273 4
## [8064] {featur,
## method,
## propos} => {detect} 0.1333333 0.8000000 4.800000 4
## [8065] {method,
## detect,
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## [8066] {method,
## network,
## detect} => {propos} 0.1000000 1.0000000 2.000000 3
## [8067] {network,
## detect,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [8068] {featur,
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## detect} => {network} 0.1000000 0.7500000 1.184211 3
## [8069] {method,
## network,
## detect} => {featur} 0.1000000 1.0000000 1.875000 3
## [8070] {featur,
## network,
## detect} => {method} 0.1000000 0.7500000 2.045455 3
## [8071] {dataset,
## detect,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [8072] {featur,
## detect,
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## [8073] {featur,
## dataset,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [8074] {dataset,
## detect,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8075] {network,
## detect,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8076] {network,
## dataset,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [8077] {featur,
## detect,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8078] {network,
## detect,
## work} => {featur} 0.1000000 1.0000000 1.875000 3
## [8079] {featur,
## network,
## detect} => {work} 0.1000000 0.7500000 1.875000 3
## [8080] {dataset,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [8081] {featur,
## detect,
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## [8082] {featur,
## dataset,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [8083] {dataset,
## detect,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [8084] {network,
## detect,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8085] {network,
## dataset,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [8086] {network,
## dataset,
## perform} => {detect} 0.1000000 0.7500000 4.500000 3
## [8087] {detect,
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## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [8088] {featur,
## detect,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [8089] {featur,
## detect,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [8090] {featur,
## detect,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [8091] {network,
## detect,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [8092] {featur,
## network,
## detect} => {perform} 0.1000000 0.7500000 1.607143 3
## [8093] {featur,
## network,
## perform} => {detect} 0.1000000 1.0000000 6.000000 3
## [8094] {dataset,
## detect,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [8095] {featur,
## dataset,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [8096] {featur,
## detect,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8097] {dataset,
## detect,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [8098] {network,
## dataset,
## detect} => {propos} 0.1000000 0.7500000 1.500000 3
## [8099] {network,
## detect,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8100] {featur,
## dataset,
## detect} => {network} 0.1333333 1.0000000 1.578947 4
## [8101] {network,
## dataset,
## detect} => {featur} 0.1333333 1.0000000 1.875000 4
## [8102] {featur,
## network,
## detect} => {dataset} 0.1333333 1.0000000 2.307692 4
## [8103] {show,
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## [8104] {featur,
## show,
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## [8105] {featur,
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## [8106] {featur,
## show,
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## [8107] {featur,
## detect,
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## [8108] {network,
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## [8109] {featur,
## network,
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## [8110] {represent,
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## [8111] {show,
## object,
## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
## [8112] {represent,
## show,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [8113] {represent,
## show,
## object} => {stateoftheart} 0.1000000 1.0000000 6.000000 3
## [8114] {represent,
## object,
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## [8115] {object,
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## [8116] {represent,
## propos,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [8117] {represent,
## object,
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## [8118] {show,
## object,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [8119] {object,
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## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [8120] {show,
## propos,
## stateoftheart} => {object} 0.1000000 1.0000000 3.750000 3
## [8121] {method,
## propos,
## stateoftheart} => {featur} 0.1000000 1.0000000 1.875000 3
## [8122] {featur,
## method,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [8123] {featur,
## propos,
## stateoftheart} => {method} 0.1000000 1.0000000 2.727273 3
## [8124] {dataset,
## propos,
## stateoftheart} => {network} 0.1000000 1.0000000 1.578947 3
## [8125] {network,
## dataset,
## stateoftheart} => {propos} 0.1000000 1.0000000 2.000000 3
## [8126] {network,
## propos,
## stateoftheart} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8127] {represent,
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## [8128] {represent,
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## stateoftheart} => {show} 0.1000000 1.0000000 1.875000 3
## [8129] {show,
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## stateoftheart} => {represent} 0.1000000 1.0000000 2.000000 3
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## [8131] {addit,
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## [8132] {architectur,
## effici,
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## [8133] {architectur,
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## [8134] {architectur,
## addit,
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## [8135] {dataset,
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## [8136] {architectur,
## dataset,
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## [8137] {architectur,
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## [8138] {architectur,
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## [8139] {propos,
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## [8140] {architectur,
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## [8141] {architectur,
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## [8143] {network,
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## [8144] {network,
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## [8145] {network,
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## [8146] {addit,
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## [8147] {dataset,
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## [8149] {dataset,
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## [8150] {addit,
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## [8151] {propos,
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## [8152] {propos,
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## [8153] {propos,
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## [8155] {network,
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## [8156] {network,
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## [8157] {network,
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## [8158] {dataset,
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## [8159] {propos,
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## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8160] {dataset,
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## [8162] {dataset,
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## [8163] {network,
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## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8164] {network,
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## effici} => {addit} 0.1000000 1.0000000 6.000000 3
## [8165] {network,
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## [8166] {propos,
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## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8167] {network,
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## [8168] {network,
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## [8169] {network,
## propos,
## addit} => {effici} 0.1000000 0.7500000 4.500000 3
## [8170] {reduc,
## comput,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [8171] {comput,
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## [8172] {reduc,
## effici,
## optim} => {comput} 0.1000000 1.0000000 4.285714 3
## [8173] {reduc,
## comput,
## optim} => {effici} 0.1000000 1.0000000 6.000000 3
## [8174] {reduc,
## comput,
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## [8176] {reduc,
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## [8177] {reduc,
## comput,
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## [8178] {reduc,
## comput,
## effici} => {improv} 0.1000000 1.0000000 3.333333 3
## [8179] {improv,
## comput,
## effici} => {reduc} 0.1000000 1.0000000 4.285714 3
## [8180] {reduc,
## improv,
## effici} => {comput} 0.1000000 1.0000000 4.285714 3
## [8181] {reduc,
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## [8184] {effici,
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## [8186] {comput,
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## [8187] {improv,
## comput,
## effici} => {optim} 0.1000000 1.0000000 4.285714 3
## [8188] {improv,
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## [8189] {improv,
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## [8190] {comput,
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## [8191] {improv,
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## [8192] {improv,
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## [8193] {improv,
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## [8194] {reduc,
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## [8195] {reduc,
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## [8196] {effici,
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## [8198] {reduc,
## effici,
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## [8199] {reduc,
## improv,
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## [8201] {reduc,
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## [8211] {architectur,
## perform,
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## [8212] {perform,
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## perform,
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## [8214] {architectur,
## effici,
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## [8215] {architectur,
## dataset,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8216] {dataset,
## effici,
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## [8217] {architectur,
## dataset,
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## [8218] {architectur,
## effici,
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## [8219] {architectur,
## propos,
## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8220] {propos,
## effici,
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## [8221] {architectur,
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## [8222] {architectur,
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## [8223] {network,
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## effici} => {work} 0.1333333 1.0000000 2.500000 4
## [8224] {network,
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## [8225] {network,
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## [8226] {architectur,
## perform,
## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8227] {network,
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## effici} => {perform} 0.1000000 0.7500000 1.607143 3
## [8228] {network,
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## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8229] {architectur,
## dataset,
## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [8230] {architectur,
## propos,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8231] {dataset,
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## effici} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8232] {architectur,
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## [8233] {architectur,
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## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8234] {network,
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## effici} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8235] {network,
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## [8236] {architectur,
## propos,
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## [8237] {network,
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## effici} => {propos} 0.1000000 0.7500000 1.500000 3
## [8238] {network,
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## [8239] {perform,
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## [8240] {network,
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## [8241] {network,
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## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8242] {network,
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## [8243] {dataset,
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## [8244] {propos,
## effici,
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## [8245] {dataset,
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## [8246] {dataset,
## effici,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8247] {network,
## effici,
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## [8248] {network,
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## effici} => {work} 0.1000000 1.0000000 2.500000 3
## [8249] {propos,
## effici,
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## [8250] {network,
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## [8251] {network,
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## [8252] {dataset,
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## effici} => {network} 0.1000000 1.0000000 1.578947 3
## [8253] {network,
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## effici} => {propos} 0.1000000 1.0000000 2.000000 3
## [8254] {network,
## propos,
## effici} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8255] {addit,
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## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8256] {dataset,
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## [8257] {dataset,
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## [8258] {dataset,
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## [8477] {data,
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## [8478] {approach,
## data,
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## [8479] {approach,
## data,
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## [8480] {approach,
## learn,
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## [8481] {data,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8482] {approach,
## data,
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## [8483] {approach,
## data,
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## [8484] {approach,
## represent,
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## [8485] {data,
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## [8486] {approach,
## data,
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## [8487] {approach,
## propos,
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## [8488] {data,
## propos,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8489] {approach,
## data,
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## [8490] {approach,
## dataset,
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## [8491] {approach,
## learn,
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## [8492] {dataset,
## learn,
## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8493] {approach,
## dataset,
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## [8494] {approach,
## represent,
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## [8495] {represent,
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## [8496] {approach,
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## [8497] {approach,
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## [8498] {approach,
## propos,
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## [8499] {dataset,
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## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [8500] {approach,
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## [8501] {approach,
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## [8502] {represent,
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## [8503] {approach,
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## [8504] {approach,
## propos,
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## [8505] {propos,
## learn,
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## [8506] {approach,
## represent,
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## [8507] {approach,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8508] {represent,
## propos,
## semant} => {approach} 0.1000000 0.7500000 1.875000 3
## [8509] {data,
## work,
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## [8510] {dataset,
## work,
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## [8511] {data,
## dataset,
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## [8512] {data,
## work,
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## [8513] {work,
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## [8514] {data,
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## [8515] {data,
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## [8516] {data,
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## [8517] {represent,
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## [8518] {data,
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## [8519] {data,
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## [8520] {data,
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## [8521] {propos,
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## [8522] {data,
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## [8523] {data,
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## [8524] {dataset,
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## [8525] {work,
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## [8526] {dataset,
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## [8527] {dataset,
## work,
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## [8528] {dataset,
## work,
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## [8529] {represent,
## work,
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## [8530] {represent,
## dataset,
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## [8531] {represent,
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## [8532] {dataset,
## work,
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## [8533] {propos,
## work,
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## [8534] {dataset,
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## [8535] {work,
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## [8536] {represent,
## work,
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## [8537] {represent,
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## [8538] {represent,
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## [8539] {work,
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## [8540] {propos,
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## [8541] {propos,
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## [8542] {propos,
## work,
## learn} => {semant} 0.1000000 0.7500000 4.500000 3
## [8543] {represent,
## work,
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## [8544] {show,
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## [8545] {represent,
## show,
## semant} => {work} 0.1000000 0.7500000 1.875000 3
## [8546] {represent,
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## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8547] {represent,
## work,
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## [8548] {propos,
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## [8549] {represent,
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## [8550] {represent,
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## work} => {semant} 0.1333333 1.0000000 6.000000 4
## [8551] {show,
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## [8552] {propos,
## work,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [8553] {show,
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## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [8554] {show,
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## work} => {semant} 0.1000000 0.7500000 4.500000 3
## [8555] {data,
## dataset,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8556] {data,
## learn,
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## [8557] {dataset,
## learn,
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## [8558] {data,
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## [8559] {data,
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## [8560] {represent,
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## [8561] {data,
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## [8562] {data,
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## [8563] {dataset,
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## [8564] {data,
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## [8565] {data,
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## [8566] {represent,
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## [8567] {data,
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## [8568] {data,
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## [8569] {propos,
## learn,
## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [8570] {data,
## represent,
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## [8571] {data,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8572] {represent,
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## semant} => {data} 0.1000000 0.7500000 1.730769 3
## [8573] {dataset,
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## [8574] {represent,
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## [8575] {represent,
## learn,
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## [8576] {dataset,
## learn,
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## [8577] {dataset,
## propos,
## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [8578] {propos,
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## [8579] {represent,
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## [8580] {dataset,
## propos,
## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [8581] {represent,
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## semant} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8582] {represent,
## learn,
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## [8583] {propos,
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## [8584] {represent,
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## [8585] {represent,
## show,
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## [8586] {represent,
## propos,
## semant} => {show} 0.1000000 0.7500000 1.406250 3
## [8587] {show,
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## [8588] {represent,
## show,
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## [8589] {network,
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## [8590] {network,
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## [8591] {architectur,
## process,
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## [8592] {process,
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## [8593] {architectur,
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## [8594] {architectur,
## process,
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## [8595] {architectur,
## process,
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## [8596] {dataset,
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## [8597] {architectur,
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## [8598] {architectur,
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## [8599] {architectur,
## process,
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## [8600] {network,
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## [8601] {network,
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## [8602] {network,
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## [8603] {process,
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## [8604] {dataset,
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## [8605] {dataset,
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## [8606] {dataset,
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## [8607] {process,
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## [8608] {network,
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## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [8609] {network,
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## [8610] {network,
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## [8611] {dataset,
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## [8612] {network,
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## [8613] {network,
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## [8614] {network,
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## [8615] {architectur,
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## [8616] {classif,
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## [8617] {classif,
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## [8618] {classif,
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## [8619] {architectur,
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## [8620] {experi,
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## [8621] {architectur,
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## [8622] {architectur,
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## [8623] {architectur,
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## [8624] {network,
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## [8625] {network,
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## [8626] {network,
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## [8627] {classif,
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## [8628] {experi,
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## [8629] {classif,
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## [8630] {classif,
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## [8631] {classif,
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## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8632] {network,
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## [8633] {classif,
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## [8634] {classif,
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## [8635] {experi,
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## [8636] {network,
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## [8637] {network,
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## [8638] {network,
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## [8639] {classif,
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## [8640] {architectur,
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## [8641] {classif,
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## [8642] {classif,
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## [8643] {classif,
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## [8644] {network,
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## [8645] {classif,
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## [8646] {classif,
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## [8647] {architectur,
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## [8648] {train,
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## [8649] {train,
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## [8650] {train,
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## [8651] {architectur,
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## [8652] {network,
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## [8653] {network,
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## [8654] {network,
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## [8655] {train,
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## [8656] {network,
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## [8657] {network,
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## [8658] {network,
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## [8659] {architectur,
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## [8660] {architectur,
## dataset,
## analysi} => {work} 0.1000000 1.0000000 2.500000 3
## [8661] {dataset,
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## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8662] {architectur,
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## [8663] {architectur,
## analysi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8664] {network,
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## analysi} => {work} 0.1000000 0.7500000 1.875000 3
## [8665] {network,
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## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [8666] {architectur,
## dataset,
## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8667] {network,
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## analysi} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8668] {network,
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## [8669] {architectur,
## propos,
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## [8670] {network,
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## [8671] {network,
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## [8672] {featur,
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## [8673] {network,
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## [8674] {featur,
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## [8675] {classif,
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## [8676] {classif,
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## [8677] {network,
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## [8678] {classif,
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## [8679] {train,
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## analysi} => {network} 0.1000000 1.0000000 1.578947 3
## [8680] {network,
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## [8681] {network,
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## [8682] {dataset,
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## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8683] {network,
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## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8684] {network,
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## [8685] {method,
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## [8686] {appli,
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## [8687] {method,
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## [8688] {method,
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## [8689] {method,
## appli,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8690] {appli,
## propos,
## addit} => {method} 0.1000000 0.7500000 2.045455 3
## [8691] {method,
## propos,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [8692] {method,
## appli,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8693] {appli,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8694] {appli,
## dataset,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8695] {dataset,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [8696] {appli,
## dataset,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8697] {appli,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8698] {appli,
## propos,
## addit} => {work} 0.1000000 0.7500000 1.875000 3
## [8699] {propos,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [8700] {appli,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8701] {appli,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8702] {network,
## appli,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8703] {network,
## addit,
## work} => {appli} 0.1000000 0.7500000 3.750000 3
## [8704] {network,
## appli,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8705] {appli,
## perform,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8706] {appli,
## propos,
## addit} => {perform} 0.1000000 0.7500000 1.607143 3
## [8707] {perform,
## propos,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [8708] {appli,
## dataset,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8709] {appli,
## propos,
## addit} => {dataset} 0.1000000 0.7500000 1.730769 3
## [8710] {dataset,
## propos,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [8711] {appli,
## dataset,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8712] {appli,
## dataset,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [8713] {network,
## appli,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8714] {network,
## dataset,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [8715] {network,
## appli,
## dataset} => {addit} 0.1000000 0.7500000 4.500000 3
## [8716] {show,
## appli,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8717] {appli,
## propos,
## addit} => {show} 0.1000000 0.7500000 1.406250 3
## [8718] {show,
## propos,
## addit} => {appli} 0.1000000 1.0000000 5.000000 3
## [8719] {show,
## appli,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8720] {appli,
## propos,
## addit} => {network} 0.1000000 0.7500000 1.184211 3
## [8721] {network,
## appli,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8722] {network,
## propos,
## addit} => {appli} 0.1000000 0.7500000 3.750000 3
## [8723] {network,
## appli,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8724] {classif,
## addit,
## imag} => {propos} 0.1000000 1.0000000 2.000000 3
## [8725] {propos,
## addit,
## imag} => {classif} 0.1000000 1.0000000 3.750000 3
## [8726] {classif,
## propos,
## addit} => {imag} 0.1000000 1.0000000 6.000000 3
## [8727] {classif,
## propos,
## imag} => {addit} 0.1000000 1.0000000 6.000000 3
## [8728] {architectur,
## addit,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8729] {architectur,
## dataset,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8730] {dataset,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8731] {architectur,
## dataset,
## work} => {addit} 0.1000000 0.7500000 4.500000 3
## [8732] {architectur,
## addit,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [8733] {architectur,
## propos,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8734] {propos,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8735] {architectur,
## propos,
## work} => {addit} 0.1000000 1.0000000 6.000000 3
## [8736] {architectur,
## addit,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [8737] {network,
## architectur,
## addit} => {work} 0.1000000 1.0000000 2.500000 3
## [8738] {network,
## addit,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8739] {architectur,
## dataset,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8740] {architectur,
## propos,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8741] {dataset,
## propos,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8742] {architectur,
## dataset,
## propos} => {addit} 0.1000000 0.7500000 4.500000 3
## [8743] {architectur,
## dataset,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [8744] {network,
## architectur,
## addit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [8745] {network,
## dataset,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8746] {architectur,
## propos,
## addit} => {network} 0.1000000 1.0000000 1.578947 3
## [8747] {network,
## architectur,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8748] {network,
## propos,
## addit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [8749] {recognit,
## addit,
## learn} => {propos} 0.1000000 1.0000000 2.000000 3
## [8750] {propos,
## recognit,
## addit} => {learn} 0.1000000 1.0000000 2.307692 3
## [8751] {propos,
## addit,
## learn} => {recognit} 0.1000000 1.0000000 3.333333 3
## [8752] {propos,
## recognit,
## learn} => {addit} 0.1000000 0.7500000 4.500000 3
## [8753] {method,
## perform,
## addit} => {propos} 0.1000000 1.0000000 2.000000 3
## [8754] {method,
## propos,
## addit} => {perform} 0.1000000 1.0000000 2.142857 3
## [8755] {perform,
## propos,
## addit} => {method} 0.1000000 1.0000000 2.727273 3
## [8756] {dataset,
## addit,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [8757] {propos,
## addit,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [8758] {dataset,
## propos,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [8759] {dataset,
## addit,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [8760] {network,
## addit,
## work} => {dataset} 0.1333333 1.0000000 2.307692 4
## [8761] {network,
## dataset,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [8762] {propos,
## addit,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [8763] {network,
## addit,
## work} => {propos} 0.1333333 1.0000000 2.000000 4
## [8764] {network,
## propos,
## addit} => {work} 0.1333333 1.0000000 2.500000 4
## [8765] {network,
## propos,
## work} => {addit} 0.1333333 0.8000000 4.800000 4
## [8766] {dataset,
## propos,
## addit} => {network} 0.1333333 1.0000000 1.578947 4
## [8767] {network,
## dataset,
## addit} => {propos} 0.1333333 1.0000000 2.000000 4
## [8768] {network,
## propos,
## addit} => {dataset} 0.1333333 1.0000000 2.307692 4
## [8769] {machin,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8770] {problem,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8771] {machin,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8772] {machin,
## problem,
## recent} => {function} 0.1000000 1.0000000 6.000000 3
## [8773] {machin,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8774] {show,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8775] {machin,
## show,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8776] {machin,
## show,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [8777] {machin,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8778] {model,
## recent,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8779] {machin,
## model,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8780] {machin,
## model,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [8781] {problem,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8782] {show,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8783] {show,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8784] {show,
## problem,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [8785] {problem,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8786] {model,
## recent,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8787] {model,
## problem,
## function} => {recent} 0.1000000 1.0000000 4.285714 3
## [8788] {model,
## problem,
## recent} => {function} 0.1000000 0.7500000 4.500000 3
## [8789] {show,
## recent,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8790] {model,
## recent,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8791] {model,
## show,
## function} => {recent} 0.1000000 0.7500000 3.214286 3
## [8792] {machin,
## problem,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8793] {machin,
## show,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8794] {show,
## problem,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8795] {machin,
## show,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [8796] {machin,
## problem,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8797] {machin,
## model,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8798] {model,
## problem,
## function} => {machin} 0.1000000 1.0000000 4.285714 3
## [8799] {machin,
## model,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [8800] {machin,
## show,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8801] {machin,
## model,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8802] {model,
## show,
## function} => {machin} 0.1000000 0.7500000 3.214286 3
## [8803] {object,
## optim,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [8804] {optim,
## problem,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8805] {object,
## problem,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8806] {object,
## optim,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [8807] {object,
## optim,
## function} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [8808] {algorithm,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8809] {algorithm,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8810] {algorithm,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8811] {object,
## optim,
## function} => {task} 0.1000000 0.7500000 2.045455 3
## [8812] {task,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8813] {task,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8814] {task,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8815] {object,
## optim,
## function} => {data} 0.1000000 0.7500000 1.730769 3
## [8816] {data,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8817] {data,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8818] {data,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8819] {object,
## optim,
## function} => {show} 0.1000000 0.7500000 1.406250 3
## [8820] {show,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8821] {show,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8822] {show,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8823] {object,
## optim,
## function} => {propos} 0.1000000 0.7500000 1.500000 3
## [8824] {propos,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8825] {object,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8826] {object,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8827] {object,
## optim,
## function} => {model} 0.1000000 0.7500000 1.406250 3
## [8828] {model,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8829] {model,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8830] {model,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8831] {object,
## optim,
## function} => {featur} 0.1000000 0.7500000 1.406250 3
## [8832] {featur,
## optim,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8833] {featur,
## object,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8834] {featur,
## object,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8835] {optim,
## problem,
## function} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [8836] {algorithm,
## optim,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8837] {algorithm,
## problem,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8838] {algorithm,
## optim,
## problem} => {function} 0.1000000 0.7500000 4.500000 3
## [8839] {task,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8840] {data,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8841] {data,
## task,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8842] {data,
## task,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8843] {task,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8844] {propos,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8845] {task,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8846] {task,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8847] {task,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8848] {featur,
## optim,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8849] {featur,
## task,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8850] {featur,
## task,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8851] {data,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8852] {propos,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8853] {data,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8854] {data,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8855] {data,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8856] {featur,
## optim,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8857] {data,
## featur,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8858] {data,
## featur,
## optim} => {function} 0.1000000 0.7500000 4.500000 3
## [8859] {show,
## optim,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8860] {model,
## optim,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8861] {model,
## show,
## function} => {optim} 0.1000000 0.7500000 3.214286 3
## [8862] {model,
## show,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8863] {propos,
## optim,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8864] {featur,
## optim,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8865] {featur,
## propos,
## function} => {optim} 0.1000000 1.0000000 4.285714 3
## [8866] {featur,
## propos,
## optim} => {function} 0.1000000 1.0000000 6.000000 3
## [8867] {object,
## problem,
## function} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [8868] {algorithm,
## object,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8869] {algorithm,
## problem,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8870] {algorithm,
## object,
## problem} => {function} 0.1000000 1.0000000 6.000000 3
## [8871] {task,
## object,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8872] {data,
## object,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8873] {data,
## task,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8874] {data,
## task,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [8875] {task,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8876] {object,
## propos,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8877] {task,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8878] {task,
## object,
## propos} => {function} 0.1000000 0.7500000 4.500000 3
## [8879] {task,
## object,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8880] {featur,
## object,
## function} => {task} 0.1000000 1.0000000 2.727273 3
## [8881] {featur,
## task,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8882] {featur,
## task,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [8883] {data,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8884] {object,
## propos,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8885] {data,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8886] {data,
## object,
## propos} => {function} 0.1000000 0.7500000 4.500000 3
## [8887] {data,
## object,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8888] {featur,
## object,
## function} => {data} 0.1000000 1.0000000 2.307692 3
## [8889] {data,
## featur,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8890] {data,
## featur,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [8891] {show,
## object,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8892] {model,
## object,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8893] {model,
## show,
## function} => {object} 0.1000000 0.7500000 2.812500 3
## [8894] {model,
## show,
## object} => {function} 0.1000000 0.7500000 4.500000 3
## [8895] {object,
## propos,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8896] {featur,
## object,
## function} => {propos} 0.1000000 1.0000000 2.000000 3
## [8897] {featur,
## propos,
## function} => {object} 0.1000000 1.0000000 3.750000 3
## [8898] {perform,
## problem,
## function} => {featur} 0.1000000 1.0000000 1.875000 3
## [8899] {featur,
## problem,
## function} => {perform} 0.1000000 1.0000000 2.142857 3
## [8900] {featur,
## perform,
## function} => {problem} 0.1000000 1.0000000 3.333333 3
## [8901] {show,
## problem,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8902] {model,
## problem,
## function} => {show} 0.1000000 1.0000000 1.875000 3
## [8903] {model,
## show,
## function} => {problem} 0.1000000 0.7500000 2.500000 3
## [8904] {model,
## show,
## problem} => {function} 0.1000000 0.7500000 4.500000 3
## [8905] {method,
## show,
## function} => {model} 0.1000000 1.0000000 1.875000 3
## [8906] {method,
## model,
## function} => {show} 0.1000000 1.0000000 1.875000 3
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## [9121] {approach,
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## [9122] {approach,
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## [9123] {approach,
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## [9124] {train,
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## [9125] {neural,
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## [9126] {train,
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## [9127] {train,
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## [9128] {network,
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## [9129] {network,
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## [9130] {approach,
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## [9131] {neural,
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## [9132] {approach,
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## [9133] {approach,
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## [9134] {network,
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## [9135] {approach,
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## [9136] {neural,
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## [9137] {network,
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## [9138] {network,
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## [9140] {train,
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## [9141] {approach,
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## [9142] {approach,
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## [9143] {approach,
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## [9144] {network,
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## [9145] {approach,
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## [9146] {train,
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## [9148] {network,
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## [9150] {approach,
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## [9152] {network,
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## [9185] {train,
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## [9192] {network,
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## [9208] {data,
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## [9213] {neural,
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## [9214] {dataset,
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## [9216] {dataset,
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## [9230] {train,
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## [9231] {dataset,
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## [9232] {train,
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## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9338] {train,
## result,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [9339] {train,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9340] {train,
## neural,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [9341] {neural,
## result,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9342] {dataset,
## result,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [9343] {dataset,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9344] {dataset,
## neural,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [9345] {neural,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9346] {network,
## result,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [9347] {network,
## neural,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9348] {algorithm,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9349] {train,
## result,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9350] {train,
## algorithm,
## specif} => {result} 0.1000000 0.7500000 2.250000 3
## [9351] {train,
## algorithm,
## result} => {specif} 0.1000000 1.0000000 6.000000 3
## [9352] {algorithm,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9353] {network,
## result,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9354] {network,
## algorithm,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9355] {network,
## algorithm,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [9356] {train,
## result,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [9357] {result,
## work,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9358] {train,
## work,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9359] {train,
## result,
## work} => {specif} 0.1000000 1.0000000 6.000000 3
## [9360] {train,
## result,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9361] {dataset,
## result,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9362] {train,
## dataset,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9363] {train,
## dataset,
## result} => {specif} 0.1000000 0.7500000 4.500000 3
## [9364] {train,
## result,
## specif} => {network} 0.1333333 1.0000000 1.578947 4
## [9365] {network,
## result,
## specif} => {train} 0.1333333 1.0000000 2.500000 4
## [9366] {network,
## train,
## specif} => {result} 0.1333333 1.0000000 3.000000 4
## [9367] {result,
## work,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9368] {network,
## result,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [9369] {network,
## work,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9370] {network,
## result,
## work} => {specif} 0.1000000 1.0000000 6.000000 3
## [9371] {dataset,
## result,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9372] {network,
## result,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9373] {network,
## dataset,
## specif} => {result} 0.1000000 1.0000000 3.000000 3
## [9374] {train,
## neural,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9375] {dataset,
## neural,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9376] {train,
## dataset,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [9377] {train,
## neural,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9378] {network,
## neural,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9379] {network,
## train,
## specif} => {neural} 0.1000000 0.7500000 2.250000 3
## [9380] {dataset,
## neural,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9381] {network,
## neural,
## specif} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9382] {network,
## dataset,
## specif} => {neural} 0.1000000 1.0000000 3.000000 3
## [9383] {train,
## algorithm,
## specif} => {network} 0.1000000 0.7500000 1.184211 3
## [9384] {network,
## algorithm,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9385] {network,
## train,
## specif} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9386] {network,
## train,
## algorithm} => {specif} 0.1000000 0.7500000 4.500000 3
## [9387] {train,
## work,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9388] {network,
## train,
## specif} => {work} 0.1000000 0.7500000 1.875000 3
## [9389] {network,
## work,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9390] {network,
## train,
## work} => {specif} 0.1000000 0.7500000 4.500000 3
## [9391] {train,
## dataset,
## specif} => {network} 0.1000000 1.0000000 1.578947 3
## [9392] {network,
## train,
## specif} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9393] {network,
## dataset,
## specif} => {train} 0.1000000 1.0000000 2.500000 3
## [9394] {result,
## layer,
## techniqu} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [9395] {algorithm,
## layer,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9396] {algorithm,
## result,
## techniqu} => {layer} 0.1000000 1.0000000 5.000000 3
## [9397] {algorithm,
## result,
## layer} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [9398] {machin,
## learn,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [9399] {featur,
## machin,
## techniqu} => {learn} 0.1000000 1.0000000 2.307692 3
## [9400] {featur,
## learn,
## techniqu} => {machin} 0.1000000 1.0000000 4.285714 3
## [9401] {featur,
## machin,
## learn} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [9402] {architectur,
## result,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [9403] {represent,
## architectur,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9404] {represent,
## result,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9405] {represent,
## architectur,
## result} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [9406] {architectur,
## result,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [9407] {featur,
## architectur,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9408] {featur,
## result,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9409] {featur,
## architectur,
## result} => {techniqu} 0.1000000 0.7500000 4.500000 3
## [9410] {represent,
## architectur,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [9411] {featur,
## architectur,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [9412] {featur,
## represent,
## techniqu} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9413] {featur,
## represent,
## architectur} => {techniqu} 0.1000000 1.0000000 6.000000 3
## [9414] {train,
## result,
## techniqu} => {network} 0.1000000 1.0000000 1.578947 3
## [9415] {network,
## result,
## techniqu} => {train} 0.1000000 1.0000000 2.500000 3
## [9416] {network,
## train,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9417] {represent,
## result,
## techniqu} => {featur} 0.1000000 1.0000000 1.875000 3
## [9418] {featur,
## result,
## techniqu} => {represent} 0.1000000 1.0000000 2.000000 3
## [9419] {featur,
## represent,
## techniqu} => {result} 0.1000000 1.0000000 3.000000 3
## [9420] {represent,
## appli,
## applic} => {propos} 0.1000000 1.0000000 2.000000 3
## [9421] {appli,
## propos,
## applic} => {represent} 0.1000000 1.0000000 2.000000 3
## [9422] {represent,
## appli,
## propos} => {applic} 0.1000000 1.0000000 4.285714 3
## [9423] {appli,
## object,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9424] {appli,
## object,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [9425] {appli,
## object,
## perform} => {featur} 0.1000000 1.0000000 1.875000 3
## [9426] {featur,
## appli,
## object} => {perform} 0.1000000 1.0000000 2.142857 3
## [9427] {featur,
## appli,
## perform} => {object} 0.1000000 0.7500000 2.812500 3
## [9428] {featur,
## object,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9429] {appli,
## object,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [9430] {featur,
## appli,
## object} => {propos} 0.1000000 1.0000000 2.000000 3
## [9431] {featur,
## appli,
## propos} => {object} 0.1000000 0.7500000 2.812500 3
## [9432] {method,
## appli,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [9433] {appli,
## architectur,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9434] {method,
## appli,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9435] {method,
## architectur,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [9436] {method,
## appli,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9437] {appli,
## architectur,
## dataset} => {method} 0.1000000 1.0000000 2.727273 3
## [9438] {method,
## appli,
## dataset} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9439] {method,
## architectur,
## dataset} => {appli} 0.1000000 1.0000000 5.000000 3
## [9440] {method,
## appli,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [9441] {appli,
## architectur,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [9442] {method,
## appli,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9443] {method,
## architectur,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9444] {method,
## appli,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [9445] {network,
## appli,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [9446] {method,
## network,
## appli} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9447] {method,
## network,
## architectur} => {appli} 0.1000000 0.7500000 3.750000 3
## [9448] {appli,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9449] {appli,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [9450] {appli,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9451] {architectur,
## dataset,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9452] {appli,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9453] {appli,
## architectur,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [9454] {architectur,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [9455] {appli,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [9456] {network,
## appli,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [9457] {network,
## appli,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [9458] {appli,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [9459] {appli,
## architectur,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9460] {appli,
## dataset,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9461] {architectur,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9462] {appli,
## architectur,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [9463] {network,
## appli,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9464] {network,
## appli,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9465] {appli,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [9466] {network,
## appli,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [9467] {network,
## appli,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [9468] {classif,
## method,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9469] {classif,
## appli,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9470] {method,
## appli,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [9471] {classif,
## method,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9472] {classif,
## method,
## appli} => {show} 0.1000000 1.0000000 1.875000 3
## [9473] {classif,
## show,
## appli} => {method} 0.1000000 1.0000000 2.727273 3
## [9474] {method,
## show,
## appli} => {classif} 0.1000000 1.0000000 3.750000 3
## [9475] {classif,
## method,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9476] {classif,
## appli,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [9477] {method,
## appli,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [9478] {classif,
## method,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9479] {classif,
## appli,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [9480] {classif,
## show,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9481] {show,
## appli,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [9482] {classif,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9483] {classif,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [9484] {classif,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9485] {classif,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9486] {classif,
## appli,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [9487] {show,
## appli,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [9488] {classif,
## show,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9489] {appli,
## perform,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [9490] {appli,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [9491] {show,
## appli,
## recognit} => {propos} 0.1000000 1.0000000 2.000000 3
## [9492] {appli,
## propos,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [9493] {show,
## appli,
## propos} => {recognit} 0.1000000 0.7500000 2.500000 3
## [9494] {show,
## propos,
## recognit} => {appli} 0.1000000 0.7500000 3.750000 3
## [9495] {approach,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9496] {appli,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [9497] {approach,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [9498] {approach,
## dataset,
## neural} => {appli} 0.1000000 0.7500000 3.750000 3
## [9499] {approach,
## appli,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [9500] {show,
## appli,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [9501] {approach,
## show,
## appli} => {neural} 0.1000000 1.0000000 3.000000 3
## [9502] {approach,
## show,
## neural} => {appli} 0.1000000 0.7500000 3.750000 3
## [9503] {approach,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9504] {appli,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [9505] {approach,
## appli,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9506] {approach,
## appli,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [9507] {network,
## appli,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [9508] {approach,
## network,
## appli} => {neural} 0.1000000 1.0000000 3.000000 3
## [9509] {appli,
## dataset,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [9510] {show,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9511] {show,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [9512] {show,
## dataset,
## neural} => {appli} 0.1000000 1.0000000 5.000000 3
## [9513] {appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9514] {appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9515] {appli,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9516] {appli,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [9517] {network,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9518] {network,
## appli,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [9519] {show,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9520] {appli,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [9521] {show,
## appli,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9522] {show,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [9523] {show,
## appli,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [9524] {network,
## appli,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [9525] {network,
## show,
## appli} => {neural} 0.1000000 1.0000000 3.000000 3
## [9526] {appli,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [9527] {network,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [9528] {network,
## appli,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [9529] {method,
## appli,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9530] {method,
## appli,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [9531] {appli,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9532] {method,
## appli,
## perform} => {show} 0.1000000 0.7500000 1.406250 3
## [9533] {method,
## show,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9534] {show,
## appli,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9535] {method,
## appli,
## perform} => {propos} 0.1333333 1.0000000 2.000000 4
## [9536] {method,
## appli,
## propos} => {perform} 0.1333333 1.0000000 2.142857 4
## [9537] {appli,
## perform,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [9538] {method,
## perform,
## propos} => {appli} 0.1333333 0.8000000 4.000000 4
## [9539] {method,
## appli,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [9540] {featur,
## method,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9541] {featur,
## appli,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [9542] {featur,
## method,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9543] {method,
## appli,
## perform} => {network} 0.1000000 0.7500000 1.184211 3
## [9544] {method,
## network,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9545] {network,
## appli,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [9546] {method,
## network,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [9547] {method,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [9548] {method,
## appli,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9549] {appli,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9550] {method,
## appli,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [9551] {method,
## network,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9552] {network,
## appli,
## dataset} => {method} 0.1000000 0.7500000 2.045455 3
## [9553] {method,
## network,
## dataset} => {appli} 0.1000000 0.7500000 3.750000 3
## [9554] {method,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9555] {method,
## appli,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [9556] {show,
## appli,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9557] {method,
## appli,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [9558] {featur,
## method,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9559] {featur,
## appli,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9560] {method,
## appli,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [9561] {method,
## network,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9562] {network,
## appli,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [9563] {approach,
## algorithm,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [9564] {algorithm,
## appli,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [9565] {approach,
## appli,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [9566] {approach,
## algorithm,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [9567] {approach,
## algorithm,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9568] {algorithm,
## appli,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [9569] {approach,
## appli,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [9570] {approach,
## algorithm,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9571] {algorithm,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9572] {algorithm,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [9573] {algorithm,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9574] {approach,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9575] {approach,
## appli,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9576] {approach,
## appli,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [9577] {approach,
## show,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9578] {show,
## appli,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [9579] {approach,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [9580] {approach,
## appli,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9581] {appli,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [9582] {approach,
## appli,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [9583] {approach,
## network,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9584] {network,
## appli,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [9585] {approach,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9586] {approach,
## appli,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [9587] {show,
## appli,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [9588] {approach,
## show,
## appli} => {network} 0.1000000 1.0000000 1.578947 3
## [9589] {approach,
## network,
## appli} => {show} 0.1000000 1.0000000 1.875000 3
## [9590] {network,
## show,
## appli} => {approach} 0.1000000 1.0000000 2.500000 3
## [9591] {approach,
## appli,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [9592] {approach,
## network,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9593] {network,
## appli,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [9594] {appli,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [9595] {appli,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9596] {appli,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [9597] {appli,
## dataset,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9598] {network,
## appli,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9599] {network,
## appli,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [9600] {appli,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [9601] {network,
## appli,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [9602] {network,
## appli,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [9603] {appli,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9604] {appli,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9605] {appli,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [9606] {network,
## appli,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9607] {network,
## appli,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [9608] {network,
## dataset,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [9609] {show,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9610] {show,
## appli,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9611] {appli,
## perform,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [9612] {featur,
## appli,
## perform} => {propos} 0.1333333 1.0000000 2.000000 4
## [9613] {featur,
## appli,
## propos} => {perform} 0.1333333 1.0000000 2.142857 4
## [9614] {network,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [9615] {network,
## appli,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [9616] {network,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [9617] {show,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [9618] {appli,
## dataset,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [9619] {show,
## appli,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9620] {show,
## appli,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [9621] {network,
## appli,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [9622] {network,
## show,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9623] {appli,
## dataset,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [9624] {network,
## appli,
## dataset} => {propos} 0.1333333 1.0000000 2.000000 4
## [9625] {network,
## appli,
## propos} => {dataset} 0.1333333 1.0000000 2.307692 4
## [9626] {show,
## appli,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [9627] {network,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [9628] {network,
## appli,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [9629] {network,
## show,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [9630] {perform,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9631] {represent,
## work,
## larg} => {perform} 0.1000000 0.7500000 1.607143 3
## [9632] {represent,
## perform,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9633] {represent,
## perform,
## work} => {larg} 0.1000000 1.0000000 6.000000 3
## [9634] {data,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9635] {dataset,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9636] {data,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9637] {data,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9638] {represent,
## work,
## larg} => {data} 0.1000000 0.7500000 1.730769 3
## [9639] {data,
## represent,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9640] {data,
## represent,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9641] {data,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9642] {featur,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9643] {data,
## featur,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9644] {data,
## featur,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9645] {dataset,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9646] {represent,
## work,
## larg} => {dataset} 0.1000000 0.7500000 1.730769 3
## [9647] {represent,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9648] {represent,
## dataset,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9649] {dataset,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9650] {featur,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9651] {featur,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9652] {represent,
## work,
## larg} => {propos} 0.1000000 0.7500000 1.500000 3
## [9653] {propos,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9654] {represent,
## propos,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9655] {represent,
## propos,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9656] {represent,
## work,
## larg} => {featur} 0.1000000 0.7500000 1.406250 3
## [9657] {featur,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9658] {featur,
## represent,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [9659] {featur,
## represent,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [9660] {data,
## dataset,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9661] {data,
## represent,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9662] {represent,
## dataset,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9663] {data,
## dataset,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9664] {data,
## featur,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9665] {featur,
## dataset,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9666] {data,
## represent,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9667] {data,
## featur,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9668] {featur,
## represent,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [9669] {represent,
## dataset,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [9670] {featur,
## dataset,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [9671] {featur,
## represent,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9672] {data,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9673] {achiev,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9674] {data,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9675] {data,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9676] {data,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9677] {achiev,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9678] {data,
## learn,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9679] {data,
## achiev,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9680] {data,
## achiev,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9681] {represent,
## achiev,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9682] {data,
## represent,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9683] {data,
## represent,
## achiev} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9684] {data,
## achiev,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9685] {featur,
## achiev,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9686] {data,
## featur,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9687] {data,
## featur,
## achiev} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9688] {achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9689] {achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9690] {dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9691] {achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9692] {achiev,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9693] {represent,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9694] {represent,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9695] {represent,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9696] {achiev,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9697] {featur,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [9698] {featur,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9699] {featur,
## achiev,
## dataset} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9700] {achiev,
## learn,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9701] {represent,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9702] {represent,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9703] {represent,
## achiev,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9704] {achiev,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9705] {featur,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9706] {featur,
## learn,
## challeng} => {achiev} 0.1000000 0.7500000 3.214286 3
## [9707] {featur,
## achiev,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9708] {represent,
## achiev,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9709] {featur,
## achiev,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9710] {featur,
## represent,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [9711] {featur,
## represent,
## achiev} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9712] {train,
## signific,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9713] {show,
## signific,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9714] {show,
## train,
## challeng} => {signific} 0.1000000 1.0000000 3.750000 3
## [9715] {show,
## train,
## signific} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9716] {show,
## object,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [9717] {object,
## propos,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9718] {show,
## propos,
## challeng} => {object} 0.1000000 1.0000000 3.750000 3
## [9719] {paper,
## train,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9720] {data,
## paper,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9721] {data,
## train,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [9722] {data,
## paper,
## train} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9723] {paper,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9724] {paper,
## learn,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9725] {train,
## learn,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [9726] {paper,
## train,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9727] {paper,
## train,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9728] {featur,
## paper,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9729] {featur,
## train,
## challeng} => {paper} 0.1000000 1.0000000 3.000000 3
## [9730] {data,
## paper,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9731] {paper,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9732] {data,
## learn,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [9733] {data,
## paper,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [9734] {data,
## paper,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9735] {featur,
## paper,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9736] {data,
## featur,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [9737] {data,
## featur,
## paper} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9738] {paper,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9739] {featur,
## paper,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9740] {featur,
## learn,
## challeng} => {paper} 0.1000000 0.7500000 2.250000 3
## [9741] {featur,
## paper,
## learn} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9742] {train,
## recognit,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [9743] {represent,
## recognit,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9744] {represent,
## train,
## challeng} => {recognit} 0.1000000 1.0000000 3.333333 3
## [9745] {represent,
## train,
## recognit} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9746] {data,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9747] {task,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9748] {data,
## learn,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [9749] {data,
## task,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9750] {show,
## task,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9751] {data,
## show,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [9752] {data,
## task,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9753] {featur,
## task,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9754] {data,
## featur,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [9755] {task,
## learn,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9756] {show,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9757] {show,
## learn,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [9758] {task,
## learn,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9759] {featur,
## task,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9760] {featur,
## learn,
## challeng} => {task} 0.1000000 0.7500000 2.045455 3
## [9761] {show,
## task,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [9762] {featur,
## task,
## challeng} => {show} 0.1000000 1.0000000 1.875000 3
## [9763] {featur,
## show,
## challeng} => {task} 0.1000000 1.0000000 2.727273 3
## [9764] {train,
## perform,
## challeng} => {propos} 0.1000000 1.0000000 2.000000 3
## [9765] {train,
## propos,
## challeng} => {perform} 0.1000000 1.0000000 2.142857 3
## [9766] {perform,
## propos,
## challeng} => {train} 0.1000000 1.0000000 2.500000 3
## [9767] {train,
## perform,
## propos} => {challeng} 0.1000000 0.7500000 4.500000 3
## [9768] {data,
## train,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [9769] {train,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [9770] {data,
## learn,
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## [9771] {data,
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## [9772] {data,
## train,
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## [9773] {featur,
## train,
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## [9774] {data,
## featur,
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## [9775] {train,
## learn,
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## [9776] {featur,
## train,
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## [9777] {featur,
## learn,
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## [9778] {featur,
## train,
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## [9779] {data,
## dataset,
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## [9780] {data,
## learn,
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## [9781] {dataset,
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## [9782] {data,
## dataset,
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## [9783] {data,
## represent,
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## [9784] {represent,
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## [9785] {data,
## dataset,
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## [9786] {data,
## featur,
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## [9787] {featur,
## dataset,
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## [9788] {data,
## learn,
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## [9789] {data,
## represent,
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## [9790] {represent,
## learn,
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## [9791] {data,
## learn,
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## [9792] {data,
## show,
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## [9793] {show,
## learn,
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## [9794] {data,
## learn,
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## [9795] {data,
## propos,
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## [9796] {propos,
## learn,
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## [9797] {data,
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## [9798] {data,
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## [9799] {model,
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## [9800] {data,
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## [9801] {data,
## featur,
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## [9802] {featur,
## learn,
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## [9803] {data,
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## [9804] {data,
## featur,
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## [9805] {featur,
## represent,
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## [9806] {data,
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## [9807] {data,
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## [9808] {featur,
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## [9809] {data,
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## [9810] {data,
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## [9811] {model,
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## [9812] {data,
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## [9813] {data,
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## [9814] {featur,
## propos,
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## [9815] {data,
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## [9816] {data,
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## [9817] {featur,
## model,
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## [9818] {dataset,
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## [9819] {represent,
## dataset,
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## [9820] {represent,
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## [9821] {dataset,
## learn,
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## [9822] {featur,
## dataset,
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## [9823] {featur,
## learn,
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## [9824] {represent,
## dataset,
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## [9825] {featur,
## dataset,
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## [9826] {featur,
## represent,
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## [9827] {represent,
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## [9828] {featur,
## learn,
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## [9829] {featur,
## represent,
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## [9830] {show,
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## [9831] {featur,
## learn,
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## [9832] {featur,
## show,
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## [9833] {propos,
## learn,
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## [9834] {model,
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## [9835] {model,
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## [9836] {propos,
## learn,
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## [9837] {featur,
## learn,
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## [9838] {featur,
## propos,
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## [9839] {model,
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## [9840] {featur,
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## [9841] {featur,
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## [9842] {model,
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## [9843] {featur,
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## [9844] {featur,
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## [9846] {propos,
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## [9847] {propos,
## imag,
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## [9848] {propos,
## demonstr,
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## [9850] {object,
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## [9851] {propos,
## recognit,
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## [9852] {object,
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## [9854] {represent,
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## [9855] {represent,
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## [9856] {represent,
## perform,
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## [9857] {perform,
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## [9858] {propos,
## recognit,
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## [9859] {perform,
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## [9860] {perform,
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## [9861] {recognit,
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## [9862] {propos,
## recognit,
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## [9863] {propos,
## imag,
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## [9864] {propos,
## recognit,
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## [9865] {recognit,
## imag,
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## [9866] {featur,
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## [9867] {featur,
## imag,
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## [9868] {represent,
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## [9869] {propos,
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## [9870] {represent,
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## [9871] {represent,
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## [9872] {propos,
## recognit,
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## [9873] {featur,
## recognit,
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## [9874] {featur,
## propos,
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## [9875] {featur,
## propos,
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## [9876] {train,
## improv,
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## [9877] {improv,
## perform,
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## [9878] {train,
## perform,
## imag} => {improv} 0.1000000 1.0000000 3.333333 3
## [9879] {train,
## improv,
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## [9880] {train,
## improv,
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## [9881] {improv,
## propos,
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## [9882] {train,
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## [9883] {train,
## improv,
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## [9884] {improv,
## perform,
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## [9885] {improv,
## propos,
## imag} => {perform} 0.1000000 1.0000000 2.142857 3
## [9886] {perform,
## propos,
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## [9887] {improv,
## perform,
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## [9888] {train,
## perform,
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## [9889] {train,
## propos,
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## [9890] {perform,
## propos,
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## [9891] {train,
## perform,
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## [9892] {represent,
## perform,
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## [9893] {perform,
## propos,
## imag} => {represent} 0.1000000 0.7500000 1.500000 3
## [9894] {represent,
## propos,
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## [9895] {show,
## perform,
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## [9896] {perform,
## propos,
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## [9897] {show,
## propos,
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## [9898] {propos,
## imag,
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## [9899] {featur,
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## [9900] {featur,
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## [9901] {approach,
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## [9902] {approach,
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## [9905] {reduc,
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## [9907] {reduc,
## optim,
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## [9908] {reduc,
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## [9909] {network,
## reduc,
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## [9910] {network,
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## [9911] {network,
## reduc,
## optim} => {exist} 0.1000000 1.0000000 6.000000 3
## [9912] {reduc,
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## [9913] {network,
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## [9914] {network,
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## [9915] {network,
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## [9919] {network,
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## [9920] {approach,
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## [9921] {approach,
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## [9922] {show,
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## [9923] {approach,
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## [9924] {approach,
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## [9925] {approach,
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## [9926] {network,
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## [9927] {approach,
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## [9928] {approach,
## show,
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## [9929] {approach,
## network,
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## [9930] {network,
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## [9931] {show,
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## [9932] {network,
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## [9933] {network,
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## [9934] {network,
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## [9936] {network,
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## [9937] {model,
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## [9938] {model,
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## [9943] {featur,
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## [9949] {featur,
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## [9950] {featur,
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## [9953] {data,
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## [9954] {data,
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## [9957] {represent,
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## [9959] {algorithm,
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## [9960] {featur,
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## [9961] {featur,
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## [9962] {featur,
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## [9964] {featur,
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## [9969] {data,
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## [9975] {train,
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## [9978] {result,
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## [9979] {network,
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## [9980] {network,
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## [9981] {network,
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## [9983] {represent,
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## [9992] {represent,
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## [9993] {neural,
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## [9994] {network,
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## [9995] {network,
## work,
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## [9996] {represent,
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## [9997] {network,
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## [9998] {network,
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## [9999] {algorithm,
## work,
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## [10000] {network,
## algorithm,
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## [10001] {network,
## work,
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## [10002] {network,
## algorithm,
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## [10003] {represent,
## algorithm,
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## [10004] {featur,
## algorithm,
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## [10005] {featur,
## represent,
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## [10006] {featur,
## represent,
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## [10007] {show,
## algorithm,
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## [10008] {model,
## algorithm,
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## [10009] {model,
## show,
## layer} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [10010] {train,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [10011] {network,
## train,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [10012] {network,
## work,
## layer} => {train} 0.1000000 0.7500000 1.875000 3
## [10013] {network,
## train,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [10014] {represent,
## work,
## layer} => {network} 0.1000000 1.0000000 1.578947 3
## [10015] {network,
## work,
## layer} => {represent} 0.1000000 0.7500000 1.500000 3
## [10016] {network,
## represent,
## layer} => {work} 0.1000000 1.0000000 2.500000 3
## [10017] {network,
## represent,
## work} => {layer} 0.1000000 0.7500000 3.750000 3
## [10018] {represent,
## layer,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [10019] {show,
## layer,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [10020] {represent,
## show,
## layer} => {learn} 0.1000000 1.0000000 2.307692 3
## [10021] {approach,
## complex,
## input} => {show} 0.1000000 1.0000000 1.875000 3
## [10022] {complex,
## input,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10023] {approach,
## complex,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [10024] {approach,
## input,
## show} => {complex} 0.1000000 0.7500000 3.750000 3
## [10025] {approach,
## complex,
## input} => {model} 0.1000000 1.0000000 1.875000 3
## [10026] {complex,
## input,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [10027] {approach,
## complex,
## model} => {input} 0.1000000 1.0000000 4.285714 3
## [10028] {approach,
## input,
## model} => {complex} 0.1000000 0.7500000 3.750000 3
## [10029] {approach,
## complex,
## input} => {featur} 0.1000000 1.0000000 1.875000 3
## [10030] {complex,
## featur,
## input} => {approach} 0.1000000 1.0000000 2.500000 3
## [10031] {approach,
## complex,
## featur} => {input} 0.1000000 1.0000000 4.285714 3
## [10032] {approach,
## featur,
## input} => {complex} 0.1000000 0.7500000 3.750000 3
## [10033] {complex,
## input,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10034] {complex,
## input,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10035] {complex,
## model,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [10036] {complex,
## input,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10037] {complex,
## featur,
## input} => {show} 0.1000000 1.0000000 1.875000 3
## [10038] {complex,
## featur,
## show} => {input} 0.1000000 0.7500000 3.214286 3
## [10039] {featur,
## input,
## show} => {complex} 0.1000000 1.0000000 5.000000 3
## [10040] {complex,
## input,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [10041] {complex,
## featur,
## input} => {model} 0.1000000 1.0000000 1.875000 3
## [10042] {complex,
## featur,
## model} => {input} 0.1000000 1.0000000 4.285714 3
## [10043] {featur,
## input,
## model} => {complex} 0.1000000 1.0000000 5.000000 3
## [10044] {classif,
## complex,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [10045] {classif,
## complex,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [10046] {complex,
## method,
## show} => {classif} 0.1000000 1.0000000 3.750000 3
## [10047] {classif,
## complex,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [10048] {classif,
## complex,
## featur} => {method} 0.1000000 1.0000000 2.727273 3
## [10049] {complex,
## featur,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [10050] {classif,
## complex,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10051] {classif,
## complex,
## featur} => {show} 0.1000000 1.0000000 1.875000 3
## [10052] {complex,
## featur,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [10053] {complex,
## method,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10054] {complex,
## featur,
## method} => {show} 0.1000000 1.0000000 1.875000 3
## [10055] {complex,
## featur,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [10056] {approach,
## complex,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10057] {approach,
## complex,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10058] {complex,
## model,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10059] {approach,
## complex,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10060] {approach,
## complex,
## featur} => {show} 0.1000000 1.0000000 1.875000 3
## [10061] {complex,
## featur,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [10062] {approach,
## complex,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [10063] {approach,
## complex,
## featur} => {model} 0.1000000 1.0000000 1.875000 3
## [10064] {complex,
## featur,
## model} => {approach} 0.1000000 1.0000000 2.500000 3
## [10065] {complex,
## show,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [10066] {complex,
## featur,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [10067] {complex,
## featur,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [10068] {complex,
## represent,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [10069] {complex,
## featur,
## represent} => {show} 0.1000000 0.7500000 1.406250 3
## [10070] {complex,
## featur,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [10071] {complex,
## featur,
## represent} => {network} 0.1000000 0.7500000 1.184211 3
## [10072] {complex,
## network,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [10073] {complex,
## featur,
## network} => {represent} 0.1000000 1.0000000 2.000000 3
## [10074] {complex,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10075] {complex,
## featur,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [10076] {complex,
## featur,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10077] {achiev,
## general,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10078] {achiev,
## dataset,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10079] {dataset,
## general,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [10080] {achiev,
## dataset,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [10081] {achiev,
## general,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [10082] {network,
## achiev,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10083] {network,
## general,
## result} => {achiev} 0.1000000 1.0000000 4.285714 3
## [10084] {network,
## achiev,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [10085] {achiev,
## dataset,
## general} => {network} 0.1000000 1.0000000 1.578947 3
## [10086] {network,
## achiev,
## general} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10087] {network,
## dataset,
## general} => {achiev} 0.1000000 1.0000000 4.285714 3
## [10088] {network,
## achiev,
## dataset} => {general} 0.1000000 0.7500000 3.750000 3
## [10089] {represent,
## general,
## signific} => {show} 0.1000000 1.0000000 1.875000 3
## [10090] {show,
## general,
## signific} => {represent} 0.1000000 1.0000000 2.000000 3
## [10091] {represent,
## show,
## general} => {signific} 0.1000000 0.7500000 2.812500 3
## [10092] {represent,
## show,
## signific} => {general} 0.1000000 1.0000000 5.000000 3
## [10093] {general,
## recognit,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [10094] {show,
## general,
## recognit} => {result} 0.1000000 0.7500000 2.250000 3
## [10095] {show,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [10096] {show,
## recognit,
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## [10097] {general,
## recognit,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [10098] {featur,
## general,
## recognit} => {result} 0.1000000 1.0000000 3.000000 3
## [10099] {featur,
## general,
## result} => {recognit} 0.1000000 1.0000000 3.333333 3
## [10100] {featur,
## recognit,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [10101] {represent,
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## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [10102] {show,
## general,
## recognit} => {represent} 0.1000000 0.7500000 1.500000 3
## [10103] {represent,
## show,
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## [10104] {show,
## general,
## recognit} => {featur} 0.1000000 0.7500000 1.406250 3
## [10105] {featur,
## general,
## recognit} => {show} 0.1000000 1.0000000 1.875000 3
## [10106] {featur,
## show,
## general} => {recognit} 0.1000000 1.0000000 3.333333 3
## [10107] {featur,
## show,
## recognit} => {general} 0.1000000 0.7500000 3.750000 3
## [10108] {algorithm,
## general,
## result} => {model} 0.1000000 1.0000000 1.875000 3
## [10109] {model,
## general,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [10110] {model,
## algorithm,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10111] {model,
## algorithm,
## result} => {general} 0.1000000 0.7500000 3.750000 3
## [10112] {dataset,
## general,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [10113] {network,
## general,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10114] {network,
## dataset,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10115] {show,
## general,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [10116] {featur,
## general,
## result} => {show} 0.1000000 1.0000000 1.875000 3
## [10117] {featur,
## show,
## general} => {result} 0.1000000 1.0000000 3.000000 3
## [10118] {featur,
## show,
## result} => {general} 0.1000000 1.0000000 5.000000 3
## [10119] {approach,
## dataset,
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## [10120] {approach,
## general,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10121] {dataset,
## general,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [10122] {approach,
## dataset,
## general} => {model} 0.1000000 1.0000000 1.875000 3
## [10123] {approach,
## model,
## general} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10124] {model,
## dataset,
## general} => {approach} 0.1000000 1.0000000 2.500000 3
## [10125] {approach,
## general,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [10126] {approach,
## model,
## general} => {propos} 0.1000000 1.0000000 2.000000 3
## [10127] {model,
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## [10128] {show,
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## [10129] {general,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [10130] {show,
## general,
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## [10131] {dataset,
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## [10132] {model,
## dataset,
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## [10133] {model,
## general,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [10134] {represent,
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## [10135] {show,
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## [10136] {represent,
## show,
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## [10137] {paper,
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## [10138] {task,
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## [10139] {paper,
## task,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10140] {paper,
## task,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10141] {paper,
## signific,
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## [10142] {train,
## signific,
## effect} => {paper} 0.1000000 1.0000000 3.000000 3
## [10143] {paper,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10144] {paper,
## train,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10145] {paper,
## signific,
## effect} => {learn} 0.1333333 1.0000000 2.307692 4
## [10146] {learn,
## signific,
## effect} => {paper} 0.1333333 1.0000000 3.000000 4
## [10147] {paper,
## learn,
## effect} => {signific} 0.1333333 1.0000000 3.750000 4
## [10148] {paper,
## learn,
## signific} => {effect} 0.1333333 1.0000000 4.285714 4
## [10149] {paper,
## signific,
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## [10150] {represent,
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## [10151] {paper,
## represent,
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## [10152] {paper,
## represent,
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## [10153] {paper,
## signific,
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## [10154] {show,
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## [10155] {paper,
## show,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10156] {paper,
## show,
## signific} => {effect} 0.1000000 1.0000000 4.285714 3
## [10157] {paper,
## signific,
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## [10158] {propos,
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## [10159] {paper,
## propos,
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## [10160] {paper,
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## [10161] {paper,
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## [10162] {featur,
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## [10163] {featur,
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## [10164] {featur,
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## [10166] {train,
## signific,
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## [10167] {task,
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## [10168] {task,
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## [10169] {task,
## signific,
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## [10170] {learn,
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## [10171] {task,
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## [10172] {task,
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## [10173] {task,
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## [10174] {featur,
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## [10175] {featur,
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## [10176] {featur,
## task,
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## [10177] {train,
## signific,
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## [10178] {learn,
## signific,
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## [10179] {train,
## learn,
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## [10180] {train,
## learn,
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## [10181] {train,
## signific,
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## [10182] {featur,
## signific,
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## [10183] {featur,
## train,
## effect} => {signific} 0.1000000 1.0000000 3.750000 3
## [10184] {featur,
## train,
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## [10185] {learn,
## signific,
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## [10186] {represent,
## signific,
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## [10187] {represent,
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## [10188] {represent,
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## [10189] {learn,
## signific,
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## [10190] {show,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10191] {show,
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## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10192] {show,
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## [10193] {learn,
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## [10194] {propos,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10195] {propos,
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## [10196] {propos,
## learn,
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## [10197] {learn,
## signific,
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## [10198] {featur,
## signific,
## effect} => {learn} 0.1000000 1.0000000 2.307692 3
## [10199] {featur,
## learn,
## effect} => {signific} 0.1000000 0.7500000 2.812500 3
## [10200] {featur,
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## [10201] {method,
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## [10202] {learn,
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## [10203] {method,
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## [10204] {method,
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## [10205] {method,
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## [10206] {represent,
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## [10207] {method,
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## [10208] {method,
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## [10209] {method,
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## [10210] {propos,
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## [10211] {method,
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## [10654] {input,
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## [10655] {approach,
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## represent} => {make} 0.1000000 0.7500000 2.500000 3
## [10656] {approach,
## input,
## make} => {show} 0.1000000 0.7500000 1.406250 3
## [10657] {input,
## make,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10658] {approach,
## input,
## show} => {make} 0.1000000 0.7500000 2.500000 3
## [10659] {approach,
## make,
## show} => {input} 0.1000000 1.0000000 4.285714 3
## [10660] {approach,
## input,
## make} => {model} 0.1000000 0.7500000 1.406250 3
## [10661] {input,
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## [10662] {approach,
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## [10663] {approach,
## make,
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## [10664] {approach,
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## [10665] {featur,
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## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [10666] {approach,
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## [10667] {input,
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## [10668] {input,
## make,
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## [10669] {make,
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## [10670] {approach,
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## [10671] {input,
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## [10672] {approach,
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## [10675] {input,
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## [10676] {paper,
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## [10680] {model,
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## [10682] {input,
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## [10683] {approach,
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## [10684] {input,
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## [10685] {approach,
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## [10686] {approach,
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## [10687] {input,
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## [10688] {approach,
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## [10691] {approach,
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## [10694] {approach,
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## [10695] {approach,
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## [10697] {approach,
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## [10698] {approach,
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## [10699] {input,
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## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [10700] {approach,
## input,
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## [10701] {approach,
## input,
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## [10702] {input,
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## represent} => {approach} 0.1000000 0.7500000 1.875000 3
## [10703] {approach,
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## [10704] {approach,
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## [10705] {featur,
## input,
## represent} => {approach} 0.1000000 1.0000000 2.500000 3
## [10706] {approach,
## input,
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## [10707] {approach,
## input,
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## [10708] {input,
## model,
## show} => {approach} 0.1333333 0.8000000 2.000000 4
## [10709] {approach,
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## [10710] {approach,
## featur,
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## [10711] {featur,
## input,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [10712] {approach,
## input,
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## [10713] {approach,
## featur,
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## [10714] {featur,
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## [10715] {input,
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## [10716] {input,
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## [10717] {input,
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## [10718] {input,
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## [10719] {input,
## model,
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## [10720] {input,
## represent,
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## [10722] {input,
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## [10723] {input,
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## [10724] {input,
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## [10725] {input,
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## [10726] {model,
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## [10727] {featur,
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## [10728] {featur,
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## [10729] {input,
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## [10730] {input,
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## [10731] {reduc,
## comput,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [10732] {reduc,
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## [10733] {comput,
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## [10734] {reduc,
## optim,
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## [10735] {reduc,
## comput,
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## [10736] {reduc,
## improv,
## comput} => {optim} 0.1000000 1.0000000 4.285714 3
## [10737] {improv,
## comput,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [10738] {reduc,
## improv,
## optim} => {comput} 0.1000000 1.0000000 4.285714 3
## [10739] {reduc,
## comput,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [10740] {reduc,
## improv,
## comput} => {problem} 0.1000000 1.0000000 3.333333 3
## [10741] {improv,
## comput,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [10742] {reduc,
## improv,
## problem} => {comput} 0.1000000 1.0000000 4.285714 3
## [10743] {comput,
## optim,
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## [10744] {improv,
## comput,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [10745] {improv,
## comput,
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## [10746] {improv,
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## [10747] {algorithm,
## improv,
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## [10748] {train,
## improv,
## comput} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [10749] {train,
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## comput} => {improv} 0.1000000 1.0000000 3.333333 3
## [10750] {train,
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## improv} => {comput} 0.1000000 0.7500000 3.214286 3
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## problem,
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## [10752] {machin,
## show,
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## [10753] {show,
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## [10754] {machin,
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## [10755] {machin,
## problem,
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## [10756] {machin,
## model,
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## [10757] {model,
## problem,
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## [10758] {machin,
## model,
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## [10759] {machin,
## algorithm,
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## [10760] {machin,
## show,
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## [10761] {show,
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## [10762] {machin,
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## [10763] {machin,
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## [10764] {machin,
## model,
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## [10765] {model,
## algorithm,
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## [10766] {machin,
## model,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [10767] {machin,
## learn,
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## [10768] {machin,
## show,
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## [10769] {machin,
## show,
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## [10770] {machin,
## learn,
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## [10771] {machin,
## model,
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## [10772] {machin,
## model,
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## [10773] {machin,
## learn,
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## [10774] {featur,
## machin,
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## [10775] {featur,
## learn,
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## [10776] {featur,
## machin,
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## [10777] {machin,
## show,
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## [10778] {machin,
## model,
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## [10779] {machin,
## model,
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## [10780] {machin,
## show,
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## [10781] {featur,
## machin,
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## [10782] {featur,
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## [10783] {featur,
## machin,
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## [10784] {machin,
## model,
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## [10785] {featur,
## machin,
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## [10786] {featur,
## model,
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## [10787] {featur,
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## [10788] {problem,
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## [10789] {show,
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## [10790] {show,
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## [10791] {show,
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## [10792] {problem,
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## [10794] {model,
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## [10795] {model,
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## [10796] {show,
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## [10797] {model,
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## [10798] {model,
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## [10799] {method,
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## [10800] {show,
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## [10801] {method,
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## [10802] {method,
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## [10804] {method,
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## [10805] {method,
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## [10806] {perform,
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## [10807] {problem,
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## [10808] {perform,
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## [10809] {perform,
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## [10810] {show,
## problem,
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## [10811] {show,
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## [10812] {perform,
## problem,
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## [10813] {model,
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## [10814] {model,
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## [10815] {problem,
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## [10816] {show,
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## [10818] {problem,
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## [10819] {model,
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## [10820] {model,
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## [10821] {show,
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## [10822] {model,
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## [10823] {model,
## show,
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## [10824] {method,
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## [10825] {method,
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## [10826] {algorithm,
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## [10827] {show,
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## [10828] {show,
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## [10829] {algorithm,
## learn,
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## [10830] {model,
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## [10831] {model,
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## [10832] {algorithm,
## learn,
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## [10833] {featur,
## algorithm,
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## [10834] {featur,
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## [10835] {featur,
## algorithm,
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## [10836] {show,
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## [10837] {model,
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## [10838] {show,
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## [10839] {featur,
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## [10840] {featur,
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## [10841] {featur,
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## [10842] {model,
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## [10843] {featur,
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## [10844] {featur,
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## [10845] {featur,
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## [10846] {approach,
## learn,
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## [10847] {approach,
## show,
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## [10848] {approach,
## learn,
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## [10849] {approach,
## model,
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## [10850] {approach,
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## [10851] {approach,
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## [10852] {perform,
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## [10853] {show,
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## [10854] {show,
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## [10855] {perform,
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## [10856] {model,
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## [10857] {show,
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## [10858] {model,
## perform,
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## [10859] {represent,
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## [10860] {represent,
## show,
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## [10861] {represent,
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## [10862] {model,
## represent,
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## [10863] {show,
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## [10864] {model,
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## [10865] {model,
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## [10866] {model,
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## [10868] {featur,
## learn,
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## [10869] {featur,
## show,
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## [10870] {model,
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## [10871] {featur,
## learn,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [10872] {featur,
## model,
## recent} => {learn} 0.1333333 1.0000000 2.307692 4
## [10873] {represent,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [10874] {model,
## represent,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [10875] {featur,
## show,
## recent} => {model} 0.1333333 1.0000000 1.875000 4
## [10876] {featur,
## model,
## recent} => {show} 0.1333333 1.0000000 1.875000 4
## [10877] {classif,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [10878] {machin,
## make,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [10879] {classif,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [10880] {classif,
## make,
## method} => {machin} 0.1000000 1.0000000 4.285714 3
## [10881] {classif,
## machin,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [10882] {approach,
## machin,
## make} => {classif} 0.1000000 1.0000000 3.750000 3
## [10883] {approach,
## classif,
## machin} => {make} 0.1000000 1.0000000 3.333333 3
## [10884] {approach,
## classif,
## make} => {machin} 0.1000000 1.0000000 4.285714 3
## [10885] {classif,
## machin,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [10886] {featur,
## machin,
## make} => {classif} 0.1000000 1.0000000 3.750000 3
## [10887] {classif,
## featur,
## machin} => {make} 0.1000000 0.7500000 2.500000 3
## [10888] {classif,
## featur,
## make} => {machin} 0.1000000 1.0000000 4.285714 3
## [10889] {machin,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [10890] {approach,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [10891] {approach,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [10892] {approach,
## make,
## method} => {machin} 0.1000000 0.7500000 3.214286 3
## [10893] {machin,
## make,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [10894] {featur,
## machin,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [10895] {featur,
## machin,
## method} => {make} 0.1000000 1.0000000 3.333333 3
## [10896] {featur,
## make,
## method} => {machin} 0.1000000 1.0000000 4.285714 3
## [10897] {approach,
## machin,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [10898] {featur,
## machin,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [10899] {approach,
## featur,
## machin} => {make} 0.1000000 1.0000000 3.333333 3
## [10900] {classif,
## machin,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [10901] {classif,
## machin,
## task} => {paper} 0.1000000 1.0000000 3.000000 3
## [10902] {machin,
## paper,
## task} => {classif} 0.1000000 1.0000000 3.750000 3
## [10903] {classif,
## paper,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [10904] {classif,
## machin,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [10905] {classif,
## machin,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [10906] {machin,
## paper,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [10907] {classif,
## paper,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [10908] {classif,
## machin,
## paper} => {featur} 0.1000000 1.0000000 1.875000 3
## [10909] {classif,
## featur,
## machin} => {paper} 0.1000000 0.7500000 2.250000 3
## [10910] {featur,
## machin,
## paper} => {classif} 0.1000000 1.0000000 3.750000 3
## [10911] {classif,
## featur,
## paper} => {machin} 0.1000000 1.0000000 4.285714 3
## [10912] {classif,
## machin,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [10913] {approach,
## classif,
## machin} => {method} 0.1000000 1.0000000 2.727273 3
## [10914] {approach,
## machin,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [10915] {classif,
## machin,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [10916] {classif,
## featur,
## machin} => {method} 0.1000000 0.7500000 2.045455 3
## [10917] {featur,
## machin,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [10918] {classif,
## machin,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [10919] {classif,
## machin,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [10920] {machin,
## task,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [10921] {classif,
## task,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [10922] {classif,
## machin,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10923] {classif,
## featur,
## machin} => {task} 0.1000000 0.7500000 2.045455 3
## [10924] {featur,
## machin,
## task} => {classif} 0.1000000 0.7500000 2.812500 3
## [10925] {classif,
## featur,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [10926] {classif,
## machin,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [10927] {classif,
## featur,
## machin} => {train} 0.1000000 0.7500000 1.875000 3
## [10928] {featur,
## machin,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [10929] {classif,
## featur,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [10930] {approach,
## classif,
## machin} => {featur} 0.1000000 1.0000000 1.875000 3
## [10931] {classif,
## featur,
## machin} => {approach} 0.1000000 0.7500000 1.875000 3
## [10932] {approach,
## featur,
## machin} => {classif} 0.1000000 1.0000000 3.750000 3
## [10933] {approach,
## classif,
## featur} => {machin} 0.1000000 0.7500000 3.214286 3
## [10934] {classif,
## machin,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [10935] {classif,
## featur,
## machin} => {learn} 0.1000000 0.7500000 1.730769 3
## [10936] {featur,
## machin,
## learn} => {classif} 0.1000000 0.7500000 2.812500 3
## [10937] {classif,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10938] {classif,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10939] {classif,
## model,
## show} => {machin} 0.1000000 0.7500000 3.214286 3
## [10940] {classif,
## machin,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [10941] {classif,
## featur,
## machin} => {show} 0.1000000 0.7500000 1.406250 3
## [10942] {featur,
## machin,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [10943] {classif,
## machin,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [10944] {classif,
## featur,
## machin} => {model} 0.1000000 0.7500000 1.406250 3
## [10945] {featur,
## machin,
## model} => {classif} 0.1000000 0.7500000 2.812500 3
## [10946] {machin,
## show,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [10947] {machin,
## model,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [10948] {model,
## show,
## problem} => {machin} 0.1000000 0.7500000 3.214286 3
## [10949] {machin,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [10950] {machin,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [10951] {machin,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [10952] {paper,
## task,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [10953] {machin,
## paper,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10954] {featur,
## machin,
## paper} => {task} 0.1000000 1.0000000 2.727273 3
## [10955] {featur,
## machin,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [10956] {featur,
## paper,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [10957] {machin,
## paper,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [10958] {featur,
## machin,
## paper} => {train} 0.1000000 1.0000000 2.500000 3
## [10959] {featur,
## machin,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [10960] {approach,
## machin,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [10961] {featur,
## machin,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [10962] {approach,
## featur,
## machin} => {method} 0.1000000 1.0000000 2.727273 3
## [10963] {machin,
## method,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10964] {machin,
## method,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [10965] {machin,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [10966] {machin,
## model,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [10967] {machin,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [10968] {featur,
## machin,
## task} => {train} 0.1000000 0.7500000 1.875000 3
## [10969] {featur,
## machin,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [10970] {featur,
## task,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [10971] {data,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [10972] {machin,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [10973] {data,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [10974] {data,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [10975] {machin,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [10976] {data,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [10977] {data,
## machin,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10978] {featur,
## machin,
## task} => {data} 0.1000000 0.7500000 1.730769 3
## [10979] {data,
## featur,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [10980] {machin,
## task,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [10981] {featur,
## machin,
## task} => {learn} 0.1000000 0.7500000 1.730769 3
## [10982] {featur,
## machin,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [10983] {machin,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10984] {featur,
## machin,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [10985] {featur,
## machin,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [10986] {machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [10987] {machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [10988] {model,
## show,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [10989] {machin,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10990] {featur,
## machin,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [10991] {featur,
## machin,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [10992] {machin,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [10993] {featur,
## machin,
## task} => {model} 0.1000000 0.7500000 1.406250 3
## [10994] {featur,
## machin,
## model} => {task} 0.1000000 0.7500000 2.045455 3
## [10995] {machin,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [10996] {machin,
## model,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [10997] {model,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [10998] {data,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [10999] {data,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [11000] {data,
## machin,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [11001] {data,
## featur,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [11002] {featur,
## machin,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [11003] {data,
## machin,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [11004] {data,
## featur,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [11005] {featur,
## machin,
## model} => {data} 0.1000000 0.7500000 1.730769 3
## [11006] {machin,
## show,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [11007] {machin,
## model,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [11008] {machin,
## show,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11009] {featur,
## machin,
## learn} => {show} 0.1000000 0.7500000 1.406250 3
## [11010] {featur,
## machin,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [11011] {machin,
## model,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11012] {featur,
## machin,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [11013] {featur,
## machin,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [11014] {machin,
## model,
## show} => {featur} 0.1333333 0.8000000 1.500000 4
## [11015] {featur,
## machin,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [11016] {featur,
## machin,
## model} => {show} 0.1333333 1.0000000 1.875000 4
## [11017] {architectur,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [11018] {classif,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11019] {classif,
## architectur,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [11020] {classif,
## architectur,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [11021] {architectur,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11022] {dataset,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11023] {architectur,
## dataset,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [11024] {architectur,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [11025] {architectur,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11026] {experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11027] {architectur,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [11028] {architectur,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11029] {architectur,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11030] {network,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11031] {network,
## architectur,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [11032] {network,
## architectur,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [11033] {classif,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11034] {dataset,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [11035] {classif,
## dataset,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [11036] {classif,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [11037] {classif,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11038] {experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [11039] {classif,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [11040] {classif,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11041] {classif,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11042] {network,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [11043] {classif,
## network,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [11044] {classif,
## network,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [11045] {dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11046] {experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11047] {dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [11048] {dataset,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11049] {dataset,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11050] {network,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11051] {network,
## dataset,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [11052] {network,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [11053] {experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11054] {network,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11055] {network,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [11056] {network,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11057] {classif,
## architectur,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11058] {architectur,
## dataset,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [11059] {classif,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11060] {classif,
## architectur,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [11061] {classif,
## architectur,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11062] {architectur,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [11063] {classif,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11064] {classif,
## architectur,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11065] {classif,
## architectur,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11066] {network,
## architectur,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [11067] {classif,
## network,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11068] {classif,
## network,
## architectur} => {process} 0.1000000 0.7500000 3.750000 3
## [11069] {architectur,
## process,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [11070] {model,
## architectur,
## process} => {recognit} 0.1000000 1.0000000 3.333333 3
## [11071] {model,
## process,
## recognit} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11072] {model,
## architectur,
## recognit} => {process} 0.1000000 1.0000000 5.000000 3
## [11073] {architectur,
## process,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [11074] {featur,
## architectur,
## process} => {recognit} 0.1000000 0.7500000 2.500000 3
## [11075] {featur,
## process,
## recognit} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11076] {featur,
## architectur,
## recognit} => {process} 0.1000000 1.0000000 5.000000 3
## [11077] {architectur,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11078] {architectur,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11079] {improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11080] {architectur,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11081] {architectur,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11082] {algorithm,
## architectur,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11083] {algorithm,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11084] {algorithm,
## architectur,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [11085] {architectur,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11086] {architectur,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11087] {improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11088] {architectur,
## improv,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11089] {architectur,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11090] {architectur,
## dataset,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11091] {dataset,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11092] {architectur,
## dataset,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [11093] {architectur,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11094] {network,
## architectur,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11095] {network,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11096] {network,
## architectur,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [11097] {architectur,
## process,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11098] {algorithm,
## architectur,
## process} => {result} 0.1000000 0.7500000 2.250000 3
## [11099] {algorithm,
## process,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11100] {algorithm,
## architectur,
## result} => {process} 0.1000000 1.0000000 5.000000 3
## [11101] {architectur,
## process,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [11102] {featur,
## architectur,
## process} => {result} 0.1000000 0.7500000 2.250000 3
## [11103] {featur,
## process,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11104] {featur,
## architectur,
## result} => {process} 0.1000000 0.7500000 3.750000 3
## [11105] {architectur,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11106] {algorithm,
## architectur,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11107] {algorithm,
## neural,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11108] {algorithm,
## architectur,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11109] {architectur,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11110] {architectur,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11111] {neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11112] {architectur,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11113] {architectur,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11114] {architectur,
## dataset,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11115] {dataset,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11116] {architectur,
## dataset,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11117] {architectur,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11118] {network,
## architectur,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11119] {network,
## neural,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11120] {network,
## architectur,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11121] {algorithm,
## architectur,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11122] {architectur,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11123] {algorithm,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11124] {algorithm,
## architectur,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11125] {algorithm,
## architectur,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11126] {architectur,
## dataset,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11127] {algorithm,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11128] {algorithm,
## architectur,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [11129] {algorithm,
## architectur,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11130] {show,
## architectur,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11131] {show,
## algorithm,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11132] {show,
## algorithm,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11133] {algorithm,
## architectur,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11134] {featur,
## architectur,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11135] {featur,
## algorithm,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11136] {featur,
## algorithm,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11137] {algorithm,
## architectur,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11138] {network,
## architectur,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11139] {network,
## algorithm,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11140] {network,
## algorithm,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11141] {architectur,
## process,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11142] {architectur,
## dataset,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11143] {dataset,
## process,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11144] {architectur,
## dataset,
## work} => {process} 0.1000000 0.7500000 3.750000 3
## [11145] {architectur,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11146] {network,
## architectur,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11147] {network,
## process,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11148] {architectur,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11149] {architectur,
## dataset,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11150] {dataset,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11151] {architectur,
## dataset,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11152] {architectur,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11153] {network,
## architectur,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11154] {network,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11155] {data,
## architectur,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11156] {featur,
## architectur,
## process} => {data} 0.1000000 0.7500000 1.730769 3
## [11157] {data,
## featur,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11158] {data,
## featur,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11159] {architectur,
## dataset,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [11160] {architectur,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11161] {dataset,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11162] {architectur,
## dataset,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11163] {architectur,
## dataset,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11164] {featur,
## architectur,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11165] {featur,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11166] {featur,
## architectur,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [11167] {architectur,
## dataset,
## process} => {network} 0.1333333 1.0000000 1.578947 4
## [11168] {network,
## architectur,
## process} => {dataset} 0.1333333 1.0000000 2.307692 4
## [11169] {network,
## dataset,
## process} => {architectur} 0.1333333 1.0000000 3.750000 4
## [11170] {network,
## architectur,
## dataset} => {process} 0.1333333 0.8000000 4.000000 4
## [11171] {architectur,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11172] {network,
## architectur,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [11173] {network,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [11174] {model,
## architectur,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11175] {featur,
## architectur,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [11176] {featur,
## model,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11177] {featur,
## model,
## architectur} => {process} 0.1000000 1.0000000 5.000000 3
## [11178] {featur,
## architectur,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11179] {network,
## architectur,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11180] {featur,
## network,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [11181] {classif,
## dataset,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11182] {classif,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11183] {dataset,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [11184] {classif,
## dataset,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [11185] {classif,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11186] {classif,
## network,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11187] {network,
## dataset,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [11188] {classif,
## network,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [11189] {classif,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11190] {classif,
## network,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [11191] {network,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [11192] {classif,
## network,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [11193] {model,
## process,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [11194] {featur,
## process,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [11195] {featur,
## model,
## process} => {recognit} 0.1000000 0.7500000 2.500000 3
## [11196] {featur,
## model,
## recognit} => {process} 0.1000000 0.7500000 3.750000 3
## [11197] {improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11198] {algorithm,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11199] {algorithm,
## neural,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11200] {algorithm,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11201] {improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11202] {improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11203] {neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11204] {improv,
## neural,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11205] {improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11206] {dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11207] {dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11208] {improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11209] {network,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11210] {network,
## neural,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11211] {network,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11212] {algorithm,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11213] {improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11214] {algorithm,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11215] {algorithm,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11216] {algorithm,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11217] {dataset,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11218] {algorithm,
## dataset,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11219] {algorithm,
## dataset,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [11220] {algorithm,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11221] {network,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11222] {network,
## algorithm,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11223] {network,
## algorithm,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [11224] {improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11225] {dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11226] {dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11227] {improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11228] {network,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11229] {network,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [11230] {network,
## improv,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11231] {dataset,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11232] {network,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11233] {network,
## dataset,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [11234] {network,
## dataset,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [11235] {algorithm,
## process,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [11236] {featur,
## process,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11237] {featur,
## algorithm,
## process} => {result} 0.1000000 0.7500000 2.250000 3
## [11238] {featur,
## algorithm,
## result} => {process} 0.1000000 1.0000000 5.000000 3
## [11239] {algorithm,
## neural,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [11240] {approach,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11241] {approach,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11242] {approach,
## algorithm,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11243] {algorithm,
## neural,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11244] {neural,
## process,
## work} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11245] {algorithm,
## process,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [11246] {algorithm,
## neural,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [11247] {algorithm,
## neural,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11248] {neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11249] {algorithm,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11250] {algorithm,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11251] {algorithm,
## neural,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11252] {dataset,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11253] {algorithm,
## dataset,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11254] {algorithm,
## dataset,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11255] {algorithm,
## neural,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11256] {show,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11257] {show,
## algorithm,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11258] {show,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11259] {algorithm,
## neural,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11260] {featur,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11261] {featur,
## algorithm,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11262] {featur,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [11263] {algorithm,
## neural,
## process} => {network} 0.1333333 1.0000000 1.578947 4
## [11264] {network,
## neural,
## process} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [11265] {network,
## algorithm,
## process} => {neural} 0.1333333 1.0000000 3.000000 4
## [11266] {network,
## algorithm,
## neural} => {process} 0.1333333 0.8000000 4.000000 4
## [11267] {approach,
## neural,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [11268] {show,
## neural,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [11269] {approach,
## show,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11270] {approach,
## show,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11271] {approach,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11272] {network,
## neural,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [11273] {approach,
## network,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11274] {neural,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11275] {network,
## neural,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11276] {network,
## process,
## work} => {neural} 0.1000000 0.7500000 2.250000 3
## [11277] {neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11278] {dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11279] {dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11280] {dataset,
## neural,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11281] {neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11282] {network,
## neural,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11283] {network,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11284] {network,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11285] {dataset,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11286] {network,
## neural,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11287] {network,
## dataset,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11288] {show,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11289] {network,
## neural,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11290] {network,
## show,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [11291] {featur,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11292] {network,
## neural,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11293] {featur,
## network,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [11294] {featur,
## network,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [11295] {approach,
## algorithm,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [11296] {show,
## algorithm,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [11297] {approach,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11298] {approach,
## show,
## algorithm} => {process} 0.1000000 1.0000000 5.000000 3
## [11299] {approach,
## algorithm,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11300] {network,
## algorithm,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [11301] {approach,
## network,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11302] {approach,
## network,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [11303] {algorithm,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11304] {network,
## algorithm,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11305] {network,
## process,
## work} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11306] {network,
## algorithm,
## work} => {process} 0.1000000 0.7500000 3.750000 3
## [11307] {algorithm,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11308] {algorithm,
## dataset,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [11309] {dataset,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11310] {algorithm,
## dataset,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11311] {algorithm,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11312] {network,
## algorithm,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11313] {network,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11314] {network,
## algorithm,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [11315] {algorithm,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11316] {network,
## algorithm,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11317] {network,
## dataset,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11318] {network,
## algorithm,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [11319] {represent,
## algorithm,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11320] {featur,
## algorithm,
## process} => {represent} 0.1000000 0.7500000 1.500000 3
## [11321] {featur,
## represent,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11322] {featur,
## represent,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [11323] {show,
## algorithm,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [11324] {model,
## algorithm,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [11325] {model,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11326] {show,
## algorithm,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11327] {featur,
## algorithm,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11328] {featur,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11329] {featur,
## show,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [11330] {show,
## algorithm,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11331] {network,
## algorithm,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11332] {network,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11333] {network,
## show,
## algorithm} => {process} 0.1000000 1.0000000 5.000000 3
## [11334] {model,
## algorithm,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11335] {featur,
## algorithm,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [11336] {featur,
## model,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11337] {featur,
## model,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [11338] {featur,
## algorithm,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11339] {network,
## algorithm,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11340] {featur,
## network,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11341] {featur,
## network,
## algorithm} => {process} 0.1000000 1.0000000 5.000000 3
## [11342] {approach,
## show,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11343] {approach,
## network,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [11344] {network,
## show,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [11345] {dataset,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11346] {network,
## process,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11347] {network,
## dataset,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11348] {featur,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11349] {network,
## process,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [11350] {featur,
## network,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [11351] {dataset,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11352] {network,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11353] {network,
## dataset,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [11354] {network,
## dataset,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [11355] {dataset,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11356] {network,
## dataset,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [11357] {network,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11358] {featur,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [11359] {network,
## dataset,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [11360] {featur,
## network,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11361] {model,
## process,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11362] {featur,
## process,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [11363] {featur,
## model,
## process} => {learn} 0.1000000 0.7500000 1.730769 3
## [11364] {model,
## show,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11365] {featur,
## show,
## process} => {model} 0.1000000 1.0000000 1.875000 3
## [11366] {featur,
## model,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [11367] {featur,
## model,
## process} => {network} 0.1000000 0.7500000 1.184211 3
## [11368] {model,
## network,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [11369] {featur,
## network,
## process} => {model} 0.1000000 0.7500000 1.406250 3
## [11370] {approach,
## demonstr,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [11371] {perform,
## demonstr,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [11372] {approach,
## perform,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [11373] {approach,
## demonstr,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11374] {dataset,
## demonstr,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [11375] {approach,
## dataset,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [11376] {approach,
## dataset,
## problem} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [11377] {perform,
## demonstr,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11378] {dataset,
## demonstr,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [11379] {dataset,
## perform,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [11380] {dataset,
## perform,
## problem} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [11381] {improv,
## perform,
## demonstr} => {show} 0.1000000 1.0000000 1.875000 3
## [11382] {show,
## improv,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [11383] {show,
## perform,
## demonstr} => {improv} 0.1000000 1.0000000 3.333333 3
## [11384] {show,
## improv,
## perform} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [11385] {approach,
## perform,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11386] {approach,
## dataset,
## demonstr} => {perform} 0.1000000 1.0000000 2.142857 3
## [11387] {dataset,
## perform,
## demonstr} => {approach} 0.1000000 1.0000000 2.500000 3
## [11388] {perform,
## demonstr,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [11389] {propos,
## demonstr,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [11390] {perform,
## propos,
## demonstr} => {work} 0.1000000 1.0000000 2.500000 3
## [11391] {perform,
## propos,
## work} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [11392] {dataset,
## demonstr,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [11393] {propos,
## demonstr,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11394] {dataset,
## propos,
## demonstr} => {work} 0.1000000 1.0000000 2.500000 3
## [11395] {dataset,
## demonstr,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [11396] {model,
## dataset,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [11397] {model,
## demonstr,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11398] {dataset,
## demonstr,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11399] {featur,
## dataset,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [11400] {featur,
## demonstr,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11401] {model,
## dataset,
## demonstr} => {featur} 0.1000000 1.0000000 1.875000 3
## [11402] {featur,
## dataset,
## demonstr} => {model} 0.1000000 1.0000000 1.875000 3
## [11403] {featur,
## model,
## demonstr} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11404] {model,
## demonstr,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11405] {featur,
## demonstr,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [11406] {featur,
## model,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [11407] {reduc,
## task,
## achiev} => {data} 0.1000000 1.0000000 2.307692 3
## [11408] {data,
## reduc,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11409] {data,
## reduc,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11410] {data,
## task,
## achiev} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11411] {reduc,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11412] {network,
## reduc,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11413] {network,
## reduc,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11414] {network,
## task,
## achiev} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11415] {data,
## reduc,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11416] {network,
## reduc,
## achiev} => {data} 0.1000000 1.0000000 2.307692 3
## [11417] {data,
## network,
## reduc} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11418] {data,
## network,
## achiev} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11419] {reduc,
## optim,
## problem} => {improv} 0.1000000 1.0000000 3.333333 3
## [11420] {reduc,
## improv,
## optim} => {problem} 0.1000000 1.0000000 3.333333 3
## [11421] {reduc,
## improv,
## problem} => {optim} 0.1000000 1.0000000 4.285714 3
## [11422] {improv,
## optim,
## problem} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11423] {method,
## reduc,
## optim} => {show} 0.1000000 1.0000000 1.875000 3
## [11424] {reduc,
## show,
## optim} => {method} 0.1000000 1.0000000 2.727273 3
## [11425] {method,
## reduc,
## show} => {optim} 0.1000000 0.7500000 3.214286 3
## [11426] {method,
## show,
## optim} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11427] {reduc,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11428] {network,
## reduc,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11429] {network,
## reduc,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [11430] {network,
## optim,
## work} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11431] {paper,
## reduc,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [11432] {data,
## paper,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [11433] {data,
## reduc,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [11434] {data,
## paper,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11435] {paper,
## reduc,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [11436] {network,
## paper,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [11437] {network,
## reduc,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [11438] {network,
## paper,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11439] {data,
## paper,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [11440] {network,
## paper,
## reduc} => {data} 0.1000000 1.0000000 2.307692 3
## [11441] {data,
## network,
## reduc} => {paper} 0.1000000 0.7500000 2.250000 3
## [11442] {data,
## network,
## paper} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11443] {method,
## reduc,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [11444] {method,
## reduc,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [11445] {reduc,
## show,
## train} => {method} 0.1000000 0.7500000 2.045455 3
## [11446] {method,
## show,
## train} => {reduc} 0.1000000 1.0000000 4.285714 3
## [11447] {approach,
## method,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11448] {method,
## reduc,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [11449] {approach,
## reduc,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [11450] {approach,
## method,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [11451] {method,
## network,
## reduc} => {approach} 0.1000000 1.0000000 2.500000 3
## [11452] {approach,
## network,
## reduc} => {method} 0.1000000 1.0000000 2.727273 3
## [11453] {method,
## reduc,
## show} => {model} 0.1000000 0.7500000 1.406250 3
## [11454] {method,
## model,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11455] {model,
## reduc,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [11456] {method,
## reduc,
## show} => {network} 0.1000000 0.7500000 1.184211 3
## [11457] {method,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11458] {network,
## reduc,
## show} => {method} 0.1000000 0.7500000 2.045455 3
## [11459] {method,
## network,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11460] {data,
## reduc,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [11461] {reduc,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [11462] {data,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [11463] {data,
## reduc,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [11464] {reduc,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [11465] {data,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [11466] {data,
## reduc,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [11467] {featur,
## reduc,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [11468] {data,
## featur,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [11469] {data,
## reduc,
## task} => {network} 0.1333333 1.0000000 1.578947 4
## [11470] {network,
## reduc,
## task} => {data} 0.1333333 1.0000000 2.307692 4
## [11471] {data,
## network,
## reduc} => {task} 0.1333333 1.0000000 2.727273 4
## [11472] {reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [11473] {reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [11474] {reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [11475] {reduc,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [11476] {featur,
## reduc,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [11477] {featur,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [11478] {reduc,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [11479] {network,
## reduc,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [11480] {network,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [11481] {reduc,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [11482] {featur,
## reduc,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [11483] {featur,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [11484] {reduc,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [11485] {network,
## reduc,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [11486] {network,
## reduc,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [11487] {network,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11488] {featur,
## reduc,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [11489] {network,
## reduc,
## task} => {featur} 0.1000000 0.7500000 1.406250 3
## [11490] {featur,
## network,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [11491] {reduc,
## show,
## train} => {network} 0.1000000 0.7500000 1.184211 3
## [11492] {network,
## reduc,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [11493] {network,
## reduc,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [11494] {network,
## show,
## train} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11495] {approach,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [11496] {approach,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11497] {network,
## reduc,
## show} => {approach} 0.1000000 0.7500000 1.875000 3
## [11498] {data,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [11499] {data,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [11500] {reduc,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [11501] {data,
## reduc,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [11502] {data,
## featur,
## reduc} => {represent} 0.1000000 1.0000000 2.000000 3
## [11503] {featur,
## reduc,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [11504] {data,
## reduc,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [11505] {data,
## network,
## reduc} => {represent} 0.1000000 0.7500000 1.500000 3
## [11506] {network,
## reduc,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [11507] {data,
## reduc,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [11508] {data,
## featur,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11509] {featur,
## reduc,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [11510] {data,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [11511] {data,
## network,
## reduc} => {show} 0.1000000 0.7500000 1.406250 3
## [11512] {network,
## reduc,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [11513] {data,
## network,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [11514] {data,
## featur,
## reduc} => {network} 0.1000000 1.0000000 1.578947 3
## [11515] {data,
## network,
## reduc} => {featur} 0.1000000 0.7500000 1.406250 3
## [11516] {featur,
## network,
## reduc} => {data} 0.1000000 1.0000000 2.307692 3
## [11517] {reduc,
## show,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [11518] {network,
## reduc,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [11519] {network,
## reduc,
## show} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11520] {reduc,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [11521] {featur,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [11522] {featur,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [11523] {reduc,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [11524] {network,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [11525] {network,
## reduc,
## show} => {represent} 0.1000000 0.7500000 1.500000 3
## [11526] {featur,
## reduc,
## represent} => {network} 0.1000000 1.0000000 1.578947 3
## [11527] {network,
## reduc,
## represent} => {featur} 0.1000000 1.0000000 1.875000 3
## [11528] {featur,
## network,
## reduc} => {represent} 0.1000000 1.0000000 2.000000 3
## [11529] {featur,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [11530] {network,
## reduc,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [11531] {featur,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [11532] {paper,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11533] {network,
## paper,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11534] {network,
## task,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [11535] {network,
## paper,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11536] {paper,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11537] {paper,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [11538] {train,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [11539] {paper,
## train,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11540] {paper,
## train,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11541] {paper,
## represent,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [11542] {represent,
## train,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [11543] {paper,
## represent,
## train} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11544] {paper,
## train,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11545] {featur,
## paper,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [11546] {featur,
## train,
## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [11547] {paper,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11548] {paper,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11549] {represent,
## achiev,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [11550] {paper,
## represent,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11551] {paper,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11552] {featur,
## paper,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11553] {featur,
## achiev,
## learn} => {paper} 0.1000000 0.7500000 2.250000 3
## [11554] {featur,
## paper,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11555] {paper,
## represent,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11556] {featur,
## paper,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11557] {featur,
## represent,
## achiev} => {paper} 0.1000000 0.7500000 2.250000 3
## [11558] {featur,
## paper,
## represent} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11559] {achiev,
## dataset,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [11560] {featur,
## achiev,
## recognit} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11561] {featur,
## achiev,
## dataset} => {recognit} 0.1000000 0.7500000 2.500000 3
## [11562] {featur,
## dataset,
## recognit} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11563] {achiev,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11564] {achiev,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [11565] {achiev,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [11566] {achiev,
## improv,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11567] {achiev,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [11568] {achiev,
## neural,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [11569] {improv,
## neural,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11570] {achiev,
## improv,
## neural} => {model} 0.1000000 1.0000000 1.875000 3
## [11571] {model,
## achiev,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [11572] {model,
## achiev,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [11573] {model,
## improv,
## neural} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11574] {achiev,
## dataset,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [11575] {achiev,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11576] {achiev,
## dataset,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [11577] {dataset,
## improv,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11578] {achiev,
## dataset,
## improv} => {model} 0.1000000 1.0000000 1.875000 3
## [11579] {model,
## achiev,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11580] {model,
## achiev,
## dataset} => {improv} 0.1000000 0.7500000 2.500000 3
## [11581] {model,
## dataset,
## improv} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11582] {achiev,
## improv,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [11583] {model,
## achiev,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [11584] {model,
## achiev,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [11585] {model,
## improv,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11586] {achiev,
## neural,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [11587] {approach,
## achiev,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [11588] {approach,
## achiev,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [11589] {approach,
## neural,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11590] {achiev,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [11591] {achiev,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [11592] {achiev,
## neural,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [11593] {neural,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11594] {achiev,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11595] {network,
## achiev,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [11596] {network,
## achiev,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [11597] {train,
## achiev,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11598] {network,
## achiev,
## result} => {train} 0.1000000 0.7500000 1.875000 3
## [11599] {network,
## train,
## achiev} => {result} 0.1000000 1.0000000 3.000000 3
## [11600] {approach,
## achiev,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [11601] {achiev,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [11602] {approach,
## achiev,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [11603] {approach,
## achiev,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11604] {network,
## achiev,
## result} => {approach} 0.1000000 0.7500000 1.875000 3
## [11605] {approach,
## network,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [11606] {approach,
## network,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11607] {achiev,
## dataset,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11608] {network,
## achiev,
## result} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11609] {network,
## achiev,
## dataset} => {result} 0.1000000 0.7500000 2.250000 3
## [11610] {achiev,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11611] {network,
## achiev,
## result} => {propos} 0.1000000 0.7500000 1.500000 3
## [11612] {network,
## achiev,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [11613] {network,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11614] {featur,
## achiev,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [11615] {network,
## achiev,
## result} => {featur} 0.1000000 0.7500000 1.406250 3
## [11616] {featur,
## network,
## achiev} => {result} 0.1000000 0.7500000 2.250000 3
## [11617] {featur,
## network,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11618] {train,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11619] {achiev,
## neural,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [11620] {train,
## achiev,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [11621] {approach,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11622] {achiev,
## neural,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [11623] {approach,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11624] {approach,
## achiev,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [11625] {network,
## achiev,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [11626] {approach,
## network,
## achiev} => {neural} 0.1000000 0.7500000 2.250000 3
## [11627] {achiev,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11628] {achiev,
## neural,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11629] {achiev,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11630] {achiev,
## dataset,
## neural} => {model} 0.1000000 1.0000000 1.875000 3
## [11631] {model,
## achiev,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11632] {model,
## achiev,
## dataset} => {neural} 0.1000000 0.7500000 2.250000 3
## [11633] {model,
## dataset,
## neural} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11634] {achiev,
## neural,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [11635] {model,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11636] {model,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11637] {model,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11638] {achiev,
## neural,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [11639] {featur,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11640] {featur,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11641] {featur,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [11642] {achiev,
## neural,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [11643] {network,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [11644] {network,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [11645] {approach,
## method,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11646] {method,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [11647] {approach,
## achiev,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [11648] {approach,
## method,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11649] {featur,
## method,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [11650] {approach,
## featur,
## achiev} => {method} 0.1000000 1.0000000 2.727273 3
## [11651] {approach,
## method,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11652] {method,
## network,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [11653] {approach,
## network,
## achiev} => {method} 0.1000000 0.7500000 2.045455 3
## [11654] {method,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [11655] {featur,
## method,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11656] {featur,
## achiev,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [11657] {method,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [11658] {method,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11659] {network,
## achiev,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [11660] {featur,
## method,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11661] {method,
## network,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11662] {featur,
## network,
## achiev} => {method} 0.1000000 0.7500000 2.045455 3
## [11663] {data,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11664] {network,
## task,
## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [11665] {data,
## network,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11666] {task,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11667] {represent,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11668] {represent,
## achiev,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [11669] {task,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11670] {featur,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11671] {featur,
## achiev,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [11672] {task,
## achiev,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [11673] {network,
## task,
## achiev} => {learn} 0.1000000 0.7500000 1.730769 3
## [11674] {network,
## achiev,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [11675] {represent,
## task,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11676] {featur,
## task,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11677] {featur,
## represent,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [11678] {represent,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11679] {network,
## task,
## achiev} => {represent} 0.1000000 0.7500000 1.500000 3
## [11680] {network,
## represent,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [11681] {featur,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11682] {network,
## task,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [11683] {featur,
## network,
## achiev} => {task} 0.1000000 0.7500000 2.045455 3
## [11684] {train,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11685] {represent,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11686] {represent,
## achiev,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [11687] {represent,
## train,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11688] {train,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11689] {featur,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11690] {featur,
## achiev,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [11691] {featur,
## train,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [11692] {represent,
## train,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11693] {featur,
## train,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11694] {featur,
## represent,
## achiev} => {train} 0.1000000 0.7500000 1.875000 3
## [11695] {approach,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [11696] {approach,
## achiev,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11697] {achiev,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [11698] {approach,
## achiev,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [11699] {approach,
## model,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11700] {model,
## achiev,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [11701] {approach,
## achiev,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [11702] {approach,
## network,
## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11703] {network,
## achiev,
## dataset} => {approach} 0.1000000 0.7500000 1.875000 3
## [11704] {approach,
## achiev,
## propos} => {model} 0.1000000 0.7500000 1.406250 3
## [11705] {approach,
## model,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11706] {model,
## achiev,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [11707] {approach,
## achiev,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [11708] {approach,
## featur,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [11709] {featur,
## achiev,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [11710] {approach,
## achiev,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [11711] {approach,
## network,
## achiev} => {propos} 0.1333333 1.0000000 2.000000 4
## [11712] {network,
## achiev,
## propos} => {approach} 0.1333333 1.0000000 2.500000 4
## [11713] {approach,
## model,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11714] {approach,
## network,
## achiev} => {model} 0.1000000 0.7500000 1.406250 3
## [11715] {model,
## network,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [11716] {approach,
## featur,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [11717] {approach,
## network,
## achiev} => {featur} 0.1000000 0.7500000 1.406250 3
## [11718] {featur,
## network,
## achiev} => {approach} 0.1000000 0.7500000 1.875000 3
## [11719] {data,
## achiev,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [11720] {data,
## achiev,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11721] {achiev,
## dataset,
## learn} => {data} 0.1000000 1.0000000 2.307692 3
## [11722] {data,
## achiev,
## dataset} => {represent} 0.1000000 1.0000000 2.000000 3
## [11723] {data,
## represent,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11724] {represent,
## achiev,
## dataset} => {data} 0.1000000 1.0000000 2.307692 3
## [11725] {data,
## achiev,
## dataset} => {featur} 0.1000000 1.0000000 1.875000 3
## [11726] {data,
## featur,
## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11727] {featur,
## achiev,
## dataset} => {data} 0.1000000 0.7500000 1.730769 3
## [11728] {data,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [11729] {data,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11730] {represent,
## achiev,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [11731] {data,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [11732] {data,
## featur,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [11733] {featur,
## achiev,
## learn} => {data} 0.1000000 0.7500000 1.730769 3
## [11734] {data,
## represent,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [11735] {data,
## featur,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [11736] {featur,
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## achiev} => {data} 0.1000000 0.7500000 1.730769 3
## [11737] {achiev,
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## [11738] {represent,
## achiev,
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## [11739] {represent,
## achiev,
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## [11740] {achiev,
## dataset,
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## [11741] {featur,
## achiev,
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## [11742] {featur,
## achiev,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11743] {represent,
## achiev,
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## [11744] {featur,
## achiev,
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## [11745] {featur,
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## achiev} => {dataset} 0.1000000 0.7500000 1.730769 3
## [11746] {show,
## achiev,
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## [11747] {featur,
## achiev,
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## [11748] {featur,
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## achiev} => {dataset} 0.1000000 1.0000000 2.307692 3
## [11749] {featur,
## show,
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## [11750] {show,
## achiev,
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## [11751] {network,
## achiev,
## dataset} => {show} 0.1000000 0.7500000 1.406250 3
## [11752] {network,
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## [11753] {achiev,
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## [11754] {model,
## achiev,
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## [11755] {model,
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## [11756] {achiev,
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## [11757] {featur,
## achiev,
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## [11758] {featur,
## achiev,
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## [11759] {achiev,
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## [11760] {network,
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## [11761] {network,
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## [11762] {model,
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## [11763] {featur,
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## [11764] {featur,
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## [11765] {model,
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## [11766] {network,
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## [11767] {model,
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## [11768] {model,
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## [11769] {featur,
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## [11770] {network,
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## [11771] {featur,
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## [11773] {achiev,
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## [11774] {represent,
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## [11775] {represent,
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## [11776] {featur,
## achiev,
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## [11777] {featur,
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## [11778] {represent,
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## [11779] {network,
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## [11780] {network,
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## [11781] {achiev,
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## [11782] {featur,
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## [11783] {featur,
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## [11784] {featur,
## achiev,
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## [11785] {network,
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## [11786] {featur,
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## [11787] {represent,
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## [11788] {featur,
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## [11789] {featur,
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## [11790] {featur,
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## [11791] {network,
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## [11792] {featur,
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## [11793] {featur,
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## [11794] {network,
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## [11795] {featur,
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## [11796] {model,
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## [11797] {featur,
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## [11798] {featur,
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## [11799] {model,
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## [11800] {network,
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## [11801] {model,
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## [11802] {model,
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## [11803] {featur,
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## [11804] {network,
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## [11805] {featur,
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## [11807] {train,
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## [11808] {train,
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## [11809] {train,
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## [11810] {object,
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## [11811] {algorithm,
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## [11812] {algorithm,
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## [11813] {algorithm,
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## [11814] {task,
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## [11815] {data,
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## [11816] {data,
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## [11817] {data,
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## [11818] {task,
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## [11819] {object,
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## [11820] {task,
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## [11823] {featur,
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## [11824] {featur,
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## [11825] {featur,
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## [11827] {object,
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## [11828] {data,
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## [11830] {data,
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## [11831] {featur,
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## [11832] {data,
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## [11835] {model,
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## [11844] {architectur,
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## [11847] {improv,
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## [11848] {architectur,
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## [11849] {architectur,
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## [11850] {network,
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## [11851] {network,
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## [11852] {network,
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## [11853] {architectur,
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## [11854] {architectur,
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## [11855] {perform,
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## [11857] {architectur,
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## [11858] {network,
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## [11859] {network,
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## [11860] {architectur,
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## [11861] {network,
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## [11862] {network,
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## [11865] {train,
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## [11866] {train,
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## [11867] {algorithm,
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## [11868] {perform,
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## [11869] {algorithm,
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## [11870] {algorithm,
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## [11871] {algorithm,
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## [11872] {show,
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## [11873] {show,
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## [11874] {show,
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## [11875] {algorithm,
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## [11876] {propos,
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## [11877] {algorithm,
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## [11878] {algorithm,
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## [11879] {train,
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## [11880] {show,
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## [11881] {show,
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## [11882] {show,
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## [11883] {perform,
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## [11885] {perform,
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## [11886] {algorithm,
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## [11887] {train,
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## [11888] {train,
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## [11889] {train,
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## [11890] {improv,
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## [11891] {improv,
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## [11892] {perform,
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## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [11893] {improv,
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## [11894] {improv,
## optim,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [11895] {network,
## improv,
## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11896] {network,
## optim,
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## [11897] {network,
## improv,
## work} => {optim} 0.1000000 1.0000000 4.285714 3
## [11898] {improv,
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## [11899] {network,
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## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11900] {network,
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## [11901] {network,
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## [11903] {algorithm,
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## [11904] {train,
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## [11905] {train,
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## [11906] {train,
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## [11907] {show,
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## [11908] {show,
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## [11909] {show,
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## [11910] {algorithm,
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## [11911] {data,
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## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11912] {data,
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## [11913] {data,
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## [11914] {algorithm,
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## [11915] {algorithm,
## propos,
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## [11916] {perform,
## propos,
## optim} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [11917] {algorithm,
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## [11918] {algorithm,
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## [11919] {featur,
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## optim} => {perform} 0.1000000 1.0000000 2.142857 3
## [11920] {featur,
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## [11921] {featur,
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## [11922] {data,
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## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11923] {featur,
## algorithm,
## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11924] {data,
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## optim} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [11925] {data,
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## [11926] {data,
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## [11927] {task,
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## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11928] {data,
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## optim} => {task} 0.1000000 1.0000000 2.727273 3
## [11929] {data,
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## optim} => {featur} 0.1000000 1.0000000 1.875000 3
## [11930] {featur,
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## [11931] {data,
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## [11932] {task,
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## [11933] {featur,
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## [11934] {featur,
## propos,
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## [11935] {perform,
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## [11936] {network,
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## [11937] {network,
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## [11938] {network,
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## [11939] {dataset,
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## [11940] {network,
## optim,
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## [11941] {network,
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## optim} => {work} 0.1000000 1.0000000 2.500000 3
## [11942] {data,
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## [11943] {featur,
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## optim} => {data} 0.1000000 1.0000000 2.307692 3
## [11944] {data,
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## [11945] {data,
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## [12166] {approach,
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## [12167] {approach,
## featur,
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## [12168] {featur,
## propos,
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## [12169] {represent,
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## [12170] {perform,
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## [12171] {data,
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## [12172] {data,
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## [12173] {represent,
## learn,
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## [12174] {show,
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## [12175] {represent,
## show,
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## [12176] {represent,
## learn,
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## [12177] {propos,
## learn,
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## [12178] {represent,
## propos,
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## [12179] {model,
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## [12180] {model,
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## [12181] {represent,
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## [12182] {featur,
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## [12183] {featur,
## represent,
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## [12184] {show,
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## [12185] {propos,
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## [12186] {show,
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## [12187] {show,
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## [12188] {model,
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## [12189] {model,
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## [12190] {show,
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## [12191] {featur,
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## [12192] {featur,
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## [12194] {featur,
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## [12200] {model,
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## [12201] {represent,
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## [12202] {featur,
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## [12203] {represent,
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## [12204] {featur,
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## [12205] {featur,
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## [12207] {architectur,
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## [12208] {classif,
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## [12209] {classif,
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## [12210] {classif,
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## [12211] {method,
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## [12212] {classif,
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## [12213] {classif,
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## [12214] {classif,
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## [12215] {approach,
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## [12216] {approach,
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## [12217] {approach,
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## [12218] {classif,
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## [12219] {architectur,
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## [12220] {classif,
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## [12221] {classif,
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## [12222] {classif,
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## [12223] {architectur,
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## [12224] {classif,
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## [12225] {classif,
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## [12226] {classif,
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## [12227] {featur,
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## [12228] {classif,
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## [12229] {classif,
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## [12230] {classif,
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## [12231] {network,
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## [12232] {classif,
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## [12233] {classif,
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## [12235] {method,
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## [12237] {method,
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## [12238] {architectur,
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## [12239] {approach,
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## [12240] {approach,
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## [12241] {approach,
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## [12249] {network,
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## [12250] {method,
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## [12254] {method,
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## [12255] {architectur,
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## [12260] {method,
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## [12261] {method,
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## [12262] {approach,
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## [12268] {approach,
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## [12269] {approach,
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## [12270] {architectur,
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## [12273] {architectur,
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## [12277] {architectur,
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## [12287] {featur,
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## [12289] {make,
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## [12290] {method,
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## [12295] {classif,
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## [12296] {classif,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [12297] {approach,
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## [12298] {approach,
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## [12299] {approach,
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## [12300] {classif,
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## [12301] {classif,
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## [12309] {approach,
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## [12310] {approach,
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## [12311] {classif,
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## [12318] {classif,
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## [12320] {classif,
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## [12325] {approach,
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## [12330] {classif,
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## [12332] {classif,
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## [12333] {network,
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## [12334] {classif,
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## [12335] {classif,
## experi,
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## [12336] {classif,
## featur,
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## [12340] {network,
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## [12341] {classif,
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## [12342] {classif,
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## [12344] {featur,
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## [12345] {classif,
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## [12346] {method,
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## [12347] {approach,
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## [12348] {approach,
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## [12350] {method,
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## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
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## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [12356] {method,
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## [12357] {method,
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## [12358] {approach,
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## [12359] {experi,
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## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [12360] {approach,
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## neural} => {approach} 0.1000000 1.0000000 2.500000 3
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## [12364] {experi,
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## [12369] {algorithm,
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## [12370] {method,
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## [12372] {method,
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## [12373] {show,
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## [12374] {method,
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## [12375] {approach,
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## [12378] {approach,
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## [12379] {approach,
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## [12380] {method,
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## [12381] {approach,
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## [12382] {approach,
## method,
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## [12383] {method,
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## [12384] {approach,
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## [12385] {approach,
## method,
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## [12386] {method,
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## [12387] {approach,
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## [12388] {approach,
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## [12389] {approach,
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## [12390] {method,
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## [12391] {approach,
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## [12392] {method,
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## [12393] {method,
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## [12394] {dataset,
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## [12395] {method,
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## [12396] {method,
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## [12397] {show,
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## [12398] {method,
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## [12399] {method,
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## [12400] {experi,
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## [12401] {method,
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## [12402] {method,
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## [12403] {model,
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## [12404] {method,
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## [12405] {method,
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## [12406] {method,
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## [12407] {show,
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## [12408] {method,
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## [12409] {method,
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## [12410] {dataset,
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## [12411] {method,
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## [12412] {method,
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## [12413] {show,
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## [12414] {method,
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## [12415] {method,
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## [12416] {model,
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## [12417] {method,
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## [12418] {method,
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## [12419] {network,
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## [12420] {algorithm,
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## [12421] {show,
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## [12422] {show,
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## [12423] {show,
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## [12424] {approach,
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## [12425] {approach,
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## [12426] {dataset,
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## [12427] {approach,
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## [12428] {approach,
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## [12429] {show,
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## [12430] {approach,
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## [12431] {approach,
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## [12432] {approach,
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## [12433] {experi,
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## [12434] {approach,
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## [12435] {approach,
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## [12436] {show,
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## [12437] {approach,
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## [12438] {approach,
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## [12439] {dataset,
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## [12440] {approach,
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## [12441] {approach,
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## [12442] {show,
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## [12443] {approach,
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## [12444] {approach,
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## [12445] {network,
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## [12446] {dataset,
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## [12447] {show,
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## [12448] {show,
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## [12449] {show,
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## [12450] {dataset,
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## [12451] {experi,
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## [12452] {dataset,
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## [12453] {show,
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## [12454] {experi,
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## [12455] {show,
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## [12457] {model,
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## [12458] {model,
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## [12459] {show,
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## [12460] {dataset,
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## [12461] {show,
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## [12462] {dataset,
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## [12463] {model,
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## [12464] {model,
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## [12465] {dataset,
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## [12466] {network,
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## [12467] {network,
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## [12468] {featur,
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## [12469] {network,
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## [12470] {featur,
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## [12472] {classif,
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## [12473] {object,
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## [12474] {classif,
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## [12475] {classif,
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## [12476] {classif,
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## [12477] {featur,
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## [12478] {classif,
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## [12479] {classif,
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## [12480] {classif,
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## [12481] {show,
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## [12482] {classif,
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## [12483] {classif,
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## [12487] {featur,
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## [12488] {classif,
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## [12491] {object,
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## [12492] {classif,
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## [12493] {classif,
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## [12494] {classif,
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## [12495] {featur,
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## [12496] {classif,
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## [12497] {classif,
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## [12498] {classif,
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## [12499] {classif,
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## [12500] {classif,
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## [12501] {featur,
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## [12502] {classif,
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## [12510] {classif,
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## [12511] {featur,
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## [12512] {object,
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## [12513] {object,
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## [12514] {object,
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## [12515] {featur,
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## [12516] {featur,
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## [12517] {object,
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## [12518] {featur,
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## [12519] {featur,
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## [12520] {object,
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## [12521] {show,
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## [12524] {object,
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## [12525] {object,
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## [12526] {perform,
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## [12528] {object,
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## [12529] {show,
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## [12530] {object,
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## [12531] {featur,
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## [12534] {method,
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## [12535] {method,
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## [12536] {method,
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## [12537] {model,
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## [12538] {method,
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## [12539] {featur,
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## [12540] {featur,
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## [12541] {method,
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## [12544] {algorithm,
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## [12545] {algorithm,
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## [12546] {algorithm,
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## [12547] {featur,
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## [12549] {featur,
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## [12565] {data,
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## [12575] {featur,
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## [12580] {model,
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## [12581] {task,
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## [12619] {dataset,
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## [12620] {featur,
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## [12621] {dataset,
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## [12622] {network,
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## [12623] {network,
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## [12625] {featur,
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## [12626] {featur,
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## [12627] {model,
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## [12628] {network,
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## [12629] {model,
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## [12630] {model,
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## [12631] {featur,
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## [12632] {network,
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## [12633] {featur,
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## [12635] {object,
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## [12637] {featur,
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## [12640] {model,
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## [12645] {featur,
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## [12646] {featur,
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## [12648] {represent,
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## [12649] {represent,
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## [12650] {featur,
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## [12653] {model,
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## [12655] {featur,
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## [12656] {featur,
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## propos} => {object} 0.1333333 1.0000000 3.750000 4
## [12657] {model,
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## [12658] {featur,
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## [12659] {featur,
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## [12660] {model,
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## [12661] {featur,
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## [12662] {model,
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## [12663] {network,
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## [12664] {model,
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## [12665] {model,
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## [12666] {network,
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## [12667] {featur,
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## [12670] {featur,
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## [12672] {classif,
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## [12674] {classif,
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## [12689] {approach,
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## [12692] {method,
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## [12696] {method,
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## [12697] {classif,
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## [12698] {approach,
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## [12699] {classif,
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## [12700] {approach,
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## [12701] {approach,
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## [12703] {classif,
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## [12704] {approach,
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## [12705] {approach,
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## [12706] {classif,
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## [12708] {architectur,
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## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12709] {classif,
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## [12710] {classif,
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## [12711] {classif,
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## [12712] {classif,
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## [12713] {classif,
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## [12714] {classif,
## featur,
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## [12715] {featur,
## architectur,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [12716] {classif,
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## propos} => {network} 0.1333333 1.0000000 1.578947 4
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## [12718] {network,
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## [12719] {classif,
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## [12720] {classif,
## featur,
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## [12721] {classif,
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## [12722] {classif,
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## [12723] {model,
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## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [12724] {featur,
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## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [12725] {featur,
## model,
## architectur} => {recognit} 0.1000000 1.0000000 3.333333 3
## [12726] {featur,
## model,
## recognit} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12727] {architectur,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [12728] {algorithm,
## architectur,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [12729] {algorithm,
## architectur,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [12730] {algorithm,
## improv,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12731] {architectur,
## improv,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [12732] {architectur,
## improv,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [12733] {architectur,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [12734] {improv,
## neural,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12735] {architectur,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12736] {architectur,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [12737] {architectur,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [12738] {architectur,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12739] {network,
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## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [12740] {network,
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## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [12741] {network,
## improv,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12742] {algorithm,
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## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [12743] {architectur,
## improv,
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## [12744] {algorithm,
## architectur,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [12745] {algorithm,
## improv,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12746] {algorithm,
## architectur,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12747] {architectur,
## dataset,
## improv} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [12748] {algorithm,
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## dataset} => {improv} 0.1000000 1.0000000 3.333333 3
## [12749] {algorithm,
## dataset,
## improv} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12750] {algorithm,
## architectur,
## improv} => {network} 0.1000000 1.0000000 1.578947 3
## [12751] {network,
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## improv} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12752] {network,
## algorithm,
## architectur} => {improv} 0.1000000 1.0000000 3.333333 3
## [12753] {network,
## algorithm,
## improv} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12754] {architectur,
## improv,
## work} => {perform} 0.1000000 1.0000000 2.142857 3
## [12755] {architectur,
## improv,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [12756] {architectur,
## perform,
## work} => {improv} 0.1000000 0.7500000 2.500000 3
## [12757] {improv,
## perform,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12758] {architectur,
## improv,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [12759] {network,
## architectur,
## improv} => {work} 0.1000000 0.7500000 1.875000 3
## [12760] {network,
## improv,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12761] {architectur,
## improv,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12762] {architectur,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [12763] {architectur,
## dataset,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [12764] {architectur,
## improv,
## perform} => {network} 0.1333333 1.0000000 1.578947 4
## [12765] {network,
## architectur,
## improv} => {perform} 0.1333333 1.0000000 2.142857 4
## [12766] {network,
## architectur,
## perform} => {improv} 0.1333333 0.8000000 2.666667 4
## [12767] {network,
## improv,
## perform} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12768] {architectur,
## dataset,
## improv} => {network} 0.1000000 1.0000000 1.578947 3
## [12769] {network,
## architectur,
## improv} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12770] {network,
## dataset,
## improv} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12771] {architectur,
## neural,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [12772] {featur,
## architectur,
## result} => {neural} 0.1000000 0.7500000 2.250000 3
## [12773] {featur,
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## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [12774] {featur,
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## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12775] {architectur,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [12776] {network,
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## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [12777] {network,
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## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [12778] {algorithm,
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## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [12779] {featur,
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## result} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12780] {featur,
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## architectur} => {result} 0.1000000 1.0000000 3.000000 3
## [12781] {featur,
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## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12782] {represent,
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## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [12783] {featur,
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## result} => {represent} 0.1000000 0.7500000 1.500000 3
## [12784] {featur,
## represent,
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## [12785] {featur,
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## result} => {network} 0.1000000 0.7500000 1.184211 3
## [12786] {network,
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## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [12787] {featur,
## network,
## result} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12788] {method,
## architectur,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [12789] {approach,
## architectur,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [12790] {approach,
## method,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12791] {approach,
## method,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12792] {method,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12793] {architectur,
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## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12794] {method,
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## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [12795] {method,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12796] {method,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12797] {network,
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## neural} => {method} 0.1000000 0.7500000 2.045455 3
## [12798] {method,
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## architectur} => {neural} 0.1000000 0.7500000 2.250000 3
## [12799] {method,
## network,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12800] {algorithm,
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## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [12801] {architectur,
## neural,
## perform} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [12802] {algorithm,
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## perform} => {neural} 0.1000000 1.0000000 3.000000 3
## [12803] {algorithm,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12804] {algorithm,
## architectur,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12805] {architectur,
## dataset,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [12806] {algorithm,
## architectur,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [12807] {algorithm,
## dataset,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12808] {algorithm,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12809] {network,
## architectur,
## neural} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12810] {network,
## algorithm,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12811] {train,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12812] {network,
## architectur,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [12813] {network,
## train,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12814] {approach,
## architectur,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [12815] {architectur,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [12816] {approach,
## architectur,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [12817] {approach,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12818] {network,
## architectur,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [12819] {approach,
## network,
## architectur} => {neural} 0.1000000 1.0000000 3.000000 3
## [12820] {architectur,
## neural,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12821] {architectur,
## dataset,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [12822] {architectur,
## dataset,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [12823] {dataset,
## neural,
## perform} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12824] {architectur,
## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [12825] {network,
## architectur,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [12826] {network,
## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12827] {architectur,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12828] {network,
## architectur,
## neural} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12829] {architectur,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12830] {network,
## architectur,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [12831] {featur,
## architectur,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [12832] {network,
## architectur,
## neural} => {featur} 0.1000000 0.7500000 1.406250 3
## [12833] {featur,
## network,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12834] {approach,
## method,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12835] {method,
## architectur,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [12836] {approach,
## architectur,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12837] {approach,
## method,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [12838] {method,
## network,
## architectur} => {approach} 0.1000000 0.7500000 1.875000 3
## [12839] {approach,
## network,
## architectur} => {method} 0.1000000 1.0000000 2.727273 3
## [12840] {method,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12841] {method,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [12842] {architectur,
## dataset,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [12843] {method,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [12844] {method,
## architectur,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12845] {architectur,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [12846] {method,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [12847] {method,
## network,
## architectur} => {perform} 0.1000000 0.7500000 1.607143 3
## [12848] {method,
## network,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12849] {method,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [12850] {method,
## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12851] {architectur,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12852] {method,
## architectur,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [12853] {method,
## network,
## architectur} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12854] {method,
## network,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12855] {method,
## architectur,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12856] {featur,
## method,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12857] {featur,
## architectur,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [12858] {method,
## architectur,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12859] {method,
## network,
## architectur} => {propos} 0.1333333 1.0000000 2.000000 4
## [12860] {network,
## architectur,
## propos} => {method} 0.1333333 0.8000000 2.181818 4
## [12861] {method,
## network,
## propos} => {architectur} 0.1333333 0.8000000 3.000000 4
## [12862] {featur,
## method,
## architectur} => {network} 0.1000000 1.0000000 1.578947 3
## [12863] {method,
## network,
## architectur} => {featur} 0.1000000 0.7500000 1.406250 3
## [12864] {algorithm,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12865] {algorithm,
## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [12866] {architectur,
## dataset,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [12867] {algorithm,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12868] {algorithm,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [12869] {network,
## algorithm,
## architectur} => {perform} 0.1000000 1.0000000 2.142857 3
## [12870] {network,
## algorithm,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12871] {algorithm,
## architectur,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [12872] {network,
## algorithm,
## architectur} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12873] {network,
## algorithm,
## dataset} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12874] {task,
## architectur,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [12875] {featur,
## task,
## architectur} => {learn} 0.1000000 1.0000000 2.307692 3
## [12876] {featur,
## architectur,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [12877] {approach,
## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12878] {approach,
## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [12879] {architectur,
## perform,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12880] {architectur,
## dataset,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [12881] {architectur,
## dataset,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [12882] {dataset,
## perform,
## work} => {architectur} 0.1000000 0.7500000 2.812500 3
## [12883] {architectur,
## perform,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [12884] {network,
## architectur,
## work} => {perform} 0.1333333 0.8000000 1.714286 4
## [12885] {network,
## architectur,
## perform} => {work} 0.1333333 0.8000000 2.000000 4
## [12886] {network,
## perform,
## work} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12887] {architectur,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [12888] {architectur,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12889] {architectur,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [12890] {architectur,
## dataset,
## work} => {featur} 0.1000000 0.7500000 1.406250 3
## [12891] {featur,
## architectur,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12892] {featur,
## architectur,
## dataset} => {work} 0.1000000 0.7500000 1.875000 3
## [12893] {architectur,
## dataset,
## work} => {network} 0.1333333 1.0000000 1.578947 4
## [12894] {network,
## architectur,
## work} => {dataset} 0.1333333 0.8000000 1.846154 4
## [12895] {network,
## architectur,
## dataset} => {work} 0.1333333 0.8000000 2.000000 4
## [12896] {architectur,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [12897] {featur,
## architectur,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [12898] {architectur,
## dataset,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [12899] {architectur,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12900] {architectur,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [12901] {architectur,
## dataset,
## perform} => {featur} 0.1000000 0.7500000 1.406250 3
## [12902] {featur,
## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12903] {featur,
## architectur,
## dataset} => {perform} 0.1000000 0.7500000 1.607143 3
## [12904] {architectur,
## dataset,
## perform} => {network} 0.1333333 1.0000000 1.578947 4
## [12905] {network,
## architectur,
## perform} => {dataset} 0.1333333 0.8000000 1.846154 4
## [12906] {network,
## architectur,
## dataset} => {perform} 0.1333333 0.8000000 1.714286 4
## [12907] {network,
## dataset,
## perform} => {architectur} 0.1333333 1.0000000 3.750000 4
## [12908] {architectur,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [12909] {network,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12910] {featur,
## architectur,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [12911] {featur,
## network,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [12912] {architectur,
## dataset,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [12913] {featur,
## architectur,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [12914] {featur,
## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12915] {architectur,
## dataset,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12916] {network,
## architectur,
## dataset} => {propos} 0.1333333 0.8000000 1.600000 4
## [12917] {network,
## architectur,
## propos} => {dataset} 0.1333333 0.8000000 1.846154 4
## [12918] {featur,
## architectur,
## dataset} => {network} 0.1333333 1.0000000 1.578947 4
## [12919] {network,
## architectur,
## dataset} => {featur} 0.1333333 0.8000000 1.500000 4
## [12920] {featur,
## network,
## architectur} => {dataset} 0.1333333 0.8000000 1.846154 4
## [12921] {featur,
## architectur,
## propos} => {network} 0.1333333 1.0000000 1.578947 4
## [12922] {network,
## architectur,
## propos} => {featur} 0.1333333 0.8000000 1.500000 4
## [12923] {featur,
## network,
## architectur} => {propos} 0.1333333 0.8000000 1.600000 4
## [12924] {featur,
## network,
## propos} => {architectur} 0.1333333 0.8000000 3.000000 4
## [12925] {classif,
## make,
## method} => {approach} 0.1000000 1.0000000 2.500000 3
## [12926] {approach,
## classif,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [12927] {approach,
## make,
## method} => {classif} 0.1000000 0.7500000 2.812500 3
## [12928] {classif,
## make,
## method} => {featur} 0.1000000 1.0000000 1.875000 3
## [12929] {classif,
## featur,
## make} => {method} 0.1000000 1.0000000 2.727273 3
## [12930] {featur,
## make,
## method} => {classif} 0.1000000 1.0000000 3.750000 3
## [12931] {approach,
## classif,
## make} => {featur} 0.1000000 1.0000000 1.875000 3
## [12932] {classif,
## featur,
## make} => {approach} 0.1000000 1.0000000 2.500000 3
## [12933] {approach,
## classif,
## featur} => {make} 0.1000000 0.7500000 2.500000 3
## [12934] {approach,
## make,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [12935] {make,
## perform,
## problem} => {approach} 0.1333333 0.8000000 2.000000 4
## [12936] {approach,
## make,
## perform} => {problem} 0.1333333 1.0000000 3.333333 4
## [12937] {approach,
## perform,
## problem} => {make} 0.1333333 0.8000000 2.666667 4
## [12938] {approach,
## make,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12939] {make,
## dataset,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [12940] {approach,
## make,
## dataset} => {problem} 0.1000000 1.0000000 3.333333 3
## [12941] {approach,
## dataset,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12942] {approach,
## make,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [12943] {make,
## problem,
## learn} => {approach} 0.1000000 1.0000000 2.500000 3
## [12944] {approach,
## make,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [12945] {approach,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [12946] {approach,
## make,
## problem} => {represent} 0.1000000 0.7500000 1.500000 3
## [12947] {make,
## represent,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [12948] {approach,
## represent,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [12949] {approach,
## make,
## problem} => {propos} 0.1000000 0.7500000 1.500000 3
## [12950] {make,
## propos,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [12951] {approach,
## make,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [12952] {approach,
## propos,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12953] {approach,
## make,
## problem} => {model} 0.1000000 0.7500000 1.406250 3
## [12954] {make,
## model,
## problem} => {approach} 0.1000000 0.7500000 1.875000 3
## [12955] {approach,
## make,
## model} => {problem} 0.1000000 0.7500000 2.500000 3
## [12956] {approach,
## model,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [12957] {approach,
## make,
## problem} => {featur} 0.1000000 0.7500000 1.406250 3
## [12958] {featur,
## make,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [12959] {approach,
## featur,
## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [12960] {make,
## dataset,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12961] {make,
## dataset,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [12962] {dataset,
## perform,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12963] {make,
## problem,
## learn} => {perform} 0.1000000 1.0000000 2.142857 3
## [12964] {make,
## perform,
## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [12965] {make,
## represent,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12966] {make,
## represent,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [12967] {represent,
## perform,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12968] {make,
## propos,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12969] {make,
## perform,
## propos} => {problem} 0.1000000 0.7500000 2.500000 3
## [12970] {make,
## perform,
## problem} => {model} 0.1333333 0.8000000 1.500000 4
## [12971] {make,
## model,
## problem} => {perform} 0.1333333 1.0000000 2.142857 4
## [12972] {model,
## perform,
## problem} => {make} 0.1333333 0.8000000 2.666667 4
## [12973] {featur,
## make,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [12974] {featur,
## make,
## perform} => {problem} 0.1000000 0.7500000 2.500000 3
## [12975] {make,
## dataset,
## problem} => {learn} 0.1000000 1.0000000 2.307692 3
## [12976] {make,
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## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [12977] {make,
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## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [12978] {dataset,
## problem,
## learn} => {make} 0.1000000 1.0000000 3.333333 3
## [12979] {make,
## dataset,
## problem} => {model} 0.1000000 1.0000000 1.875000 3
## [12980] {make,
## model,
## problem} => {dataset} 0.1000000 0.7500000 1.730769 3
## [12981] {make,
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## [12982] {model,
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## problem} => {make} 0.1000000 1.0000000 3.333333 3
## [12983] {make,
## problem,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [12984] {make,
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## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [12985] {make,
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## learn} => {problem} 0.1000000 0.7500000 2.500000 3
## [12986] {model,
## problem,
## learn} => {make} 0.1000000 0.7500000 2.500000 3
## [12987] {make,
## represent,
## problem} => {propos} 0.1000000 1.0000000 2.000000 3
## [12988] {make,
## propos,
## problem} => {represent} 0.1000000 1.0000000 2.000000 3
## [12989] {represent,
## propos,
## problem} => {make} 0.1000000 0.7500000 2.500000 3
## [12990] {make,
## method,
## paper} => {approach} 0.1000000 0.7500000 1.875000 3
## [12991] {approach,
## make,
## paper} => {method} 0.1000000 1.0000000 2.727273 3
## [12992] {approach,
## make,
## method} => {paper} 0.1000000 0.7500000 2.250000 3
## [12993] {approach,
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## [12994] {make,
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## [12995] {make,
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## [12996] {make,
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## [12997] {method,
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## [12998] {make,
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## [12999] {make,
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## [13000] {make,
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## [13001] {method,
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## show} => {make} 0.1000000 1.0000000 3.333333 3
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## [13003] {make,
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## [13004] {make,
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## [13005] {method,
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## paper} => {make} 0.1000000 1.0000000 3.333333 3
## [13006] {make,
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## [13007] {make,
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## [13008] {make,
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## [13009] {paper,
## represent,
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## [13010] {make,
## paper,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [13011] {featur,
## make,
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## [13012] {featur,
## make,
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## [13013] {approach,
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## [13015] {approach,
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## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13235] {make,
## propos,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13236] {make,
## dataset,
## learn} => {model} 0.1333333 1.0000000 1.875000 4
## [13237] {make,
## model,
## dataset} => {learn} 0.1333333 1.0000000 2.307692 4
## [13238] {make,
## model,
## learn} => {dataset} 0.1333333 1.0000000 2.307692 4
## [13239] {make,
## dataset,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13240] {featur,
## make,
## dataset} => {learn} 0.1000000 1.0000000 2.307692 3
## [13241] {featur,
## make,
## learn} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13242] {make,
## represent,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [13243] {make,
## dataset,
## propos} => {represent} 0.1000000 1.0000000 2.000000 3
## [13244] {make,
## represent,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [13245] {make,
## model,
## dataset} => {represent} 0.1000000 0.7500000 1.500000 3
## [13246] {make,
## model,
## represent} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13247] {model,
## represent,
## dataset} => {make} 0.1000000 0.7500000 2.500000 3
## [13248] {make,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [13249] {make,
## model,
## dataset} => {propos} 0.1000000 0.7500000 1.500000 3
## [13250] {make,
## model,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13251] {make,
## model,
## dataset} => {featur} 0.1000000 0.7500000 1.406250 3
## [13252] {featur,
## make,
## dataset} => {model} 0.1000000 1.0000000 1.875000 3
## [13253] {featur,
## make,
## model} => {dataset} 0.1000000 0.7500000 1.730769 3
## [13254] {make,
## represent,
## learn} => {propos} 0.1333333 1.0000000 2.000000 4
## [13255] {make,
## propos,
## learn} => {represent} 0.1333333 1.0000000 2.000000 4
## [13256] {make,
## represent,
## propos} => {learn} 0.1333333 0.8000000 1.846154 4
## [13257] {make,
## represent,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13258] {make,
## model,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13259] {make,
## model,
## represent} => {learn} 0.1000000 0.7500000 1.730769 3
## [13260] {make,
## represent,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13261] {featur,
## make,
## learn} => {represent} 0.1000000 0.7500000 1.500000 3
## [13262] {make,
## propos,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13263] {make,
## model,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13264] {make,
## model,
## propos} => {learn} 0.1000000 1.0000000 2.307692 3
## [13265] {make,
## propos,
## learn} => {featur} 0.1000000 0.7500000 1.406250 3
## [13266] {featur,
## make,
## learn} => {propos} 0.1000000 0.7500000 1.500000 3
## [13267] {featur,
## make,
## propos} => {learn} 0.1000000 0.7500000 1.730769 3
## [13268] {make,
## model,
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## [13269] {featur,
## make,
## learn} => {model} 0.1000000 0.7500000 1.406250 3
## [13270] {featur,
## make,
## model} => {learn} 0.1000000 0.7500000 1.730769 3
## [13271] {make,
## model,
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## [13272] {make,
## model,
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## [13273] {model,
## represent,
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## [13274] {make,
## represent,
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## [13275] {featur,
## make,
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## [13276] {featur,
## make,
## propos} => {represent} 0.1333333 1.0000000 2.000000 4
## [13277] {make,
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## [13278] {featur,
## make,
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## [13279] {classif,
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## [13280] {classif,
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## [13281] {classif,
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## [13282] {method,
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## [13283] {classif,
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## [13286] {classif,
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## [13287] {classif,
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## [13288] {classif,
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## [13289] {classif,
## show,
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## [13290] {classif,
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## [13291] {show,
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## [13292] {classif,
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## [13293] {classif,
## propos,
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## problem,
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## [13299] {classif,
## show,
## problem} => {learn} 0.1000000 0.7500000 1.730769 3
## [13300] {classif,
## show,
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## [13301] {show,
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## [13302] {classif,
## problem,
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## [13303] {classif,
## featur,
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## [13304] {featur,
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## [13305] {classif,
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## [13306] {classif,
## propos,
## problem} => {show} 0.1000000 1.0000000 1.875000 3
## [13307] {classif,
## show,
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## [13308] {show,
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## problem} => {classif} 0.1000000 0.7500000 2.812500 3
## [13309] {classif,
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## [13310] {classif,
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## [13311] {featur,
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## [13315] {paper,
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## [13323] {classif,
## propos,
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## [13325] {featur,
## propos,
## recognit} => {classif} 0.1000000 0.7500000 2.812500 3
## [13326] {classif,
## method,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [13327] {approach,
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## [13328] {approach,
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## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [13329] {classif,
## method,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [13330] {classif,
## improv,
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## [13332] {method,
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## [13333] {classif,
## method,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13334] {classif,
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## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [13335] {classif,
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## [13336] {method,
## dataset,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [13337] {classif,
## method,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [13338] {classif,
## show,
## improv} => {method} 0.1000000 1.0000000 2.727273 3
## [13339] {method,
## show,
## improv} => {classif} 0.1000000 0.7500000 2.812500 3
## [13340] {approach,
## classif,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [13341] {classif,
## improv,
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## [13342] {approach,
## classif,
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## [13343] {approach,
## improv,
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## [13344] {approach,
## classif,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13345] {classif,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [13346] {approach,
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## [13347] {approach,
## dataset,
## improv} => {classif} 0.1000000 0.7500000 2.812500 3
## [13348] {approach,
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## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [13349] {classif,
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## [13350] {approach,
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## [13351] {approach,
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## [13352] {classif,
## improv,
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## [13353] {classif,
## dataset,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
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## [13355] {classif,
## improv,
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## [13356] {classif,
## show,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [13357] {show,
## improv,
## perform} => {classif} 0.1000000 0.7500000 2.812500 3
## [13358] {classif,
## dataset,
## improv} => {show} 0.1000000 1.0000000 1.875000 3
## [13359] {classif,
## show,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [13360] {classif,
## show,
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## [13361] {show,
## dataset,
## improv} => {classif} 0.1000000 1.0000000 3.750000 3
## [13362] {classif,
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## [13363] {approach,
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## [13364] {approach,
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## [13365] {classif,
## method,
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## [13366] {classif,
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## [13367] {classif,
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## [13368] {method,
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## [13369] {classif,
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## [13370] {classif,
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## [13371] {classif,
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## [13372] {method,
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## [13373] {approach,
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## [13374] {classif,
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## [13375] {approach,
## classif,
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## [13376] {approach,
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## [13377] {classif,
## network,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [13378] {approach,
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## [13379] {classif,
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## [13380] {classif,
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## [13381] {classif,
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## [13384] {approach,
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## [13388] {method,
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## [13391] {approach,
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## [13393] {approach,
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## [13399] {classif,
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## [13400] {approach,
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## [13401] {approach,
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## [13402] {classif,
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## [13403] {approach,
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## [13404] {approach,
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## [13406] {classif,
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## [13427] {featur,
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## [13450] {classif,
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## [13453] {algorithm,
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## [13455] {classif,
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## [13456] {classif,
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## [13457] {show,
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## [13459] {classif,
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## [13460] {classif,
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## [13461] {featur,
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## [13499] {classif,
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## [13502] {classif,
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## [13506] {approach,
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## [13507] {classif,
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## [13628] {dataset,
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## [13629] {approach,
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## [13630] {approach,
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## [13631] {show,
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## propos,
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## [13636] {approach,
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## neural,
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## [14097] {algorithm,
## improv,
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## [14098] {algorithm,
## improv,
## neural} => {result} 0.1000000 0.7500000 2.250000 3
## [14099] {algorithm,
## neural,
## result} => {improv} 0.1000000 1.0000000 3.333333 3
## [14100] {improv,
## neural,
## result} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14101] {dataset,
## improv,
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## [14102] {dataset,
## neural,
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## [14103] {improv,
## neural,
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## [14104] {network,
## improv,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [14105] {network,
## improv,
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## [14106] {algorithm,
## improv,
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## [14107] {dataset,
## improv,
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## [14108] {algorithm,
## dataset,
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## [14109] {algorithm,
## dataset,
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## [14110] {algorithm,
## improv,
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## [14111] {network,
## improv,
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## [14112] {network,
## algorithm,
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## [14113] {network,
## algorithm,
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## [14114] {dataset,
## improv,
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## [14115] {network,
## improv,
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## [14116] {network,
## dataset,
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## [14117] {algorithm,
## improv,
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## [14118] {train,
## improv,
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## [14119] {train,
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## [14120] {train,
## algorithm,
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## [14121] {algorithm,
## improv,
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## [14122] {approach,
## improv,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14123] {approach,
## algorithm,
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## [14124] {approach,
## algorithm,
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## [14125] {algorithm,
## improv,
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## [14126] {improv,
## neural,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14127] {algorithm,
## improv,
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## [14128] {algorithm,
## neural,
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## [14129] {algorithm,
## improv,
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## [14130] {dataset,
## improv,
## neural} => {algorithm} 0.1333333 0.8000000 2.000000 4
## [14131] {algorithm,
## dataset,
## improv} => {neural} 0.1333333 1.0000000 3.000000 4
## [14132] {algorithm,
## dataset,
## neural} => {improv} 0.1333333 1.0000000 3.333333 4
## [14133] {algorithm,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14134] {improv,
## neural,
## propos} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [14135] {algorithm,
## improv,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [14136] {algorithm,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [14137] {algorithm,
## improv,
## neural} => {network} 0.1333333 1.0000000 1.578947 4
## [14138] {network,
## improv,
## neural} => {algorithm} 0.1333333 1.0000000 2.500000 4
## [14139] {network,
## algorithm,
## improv} => {neural} 0.1333333 1.0000000 3.000000 4
## [14140] {network,
## algorithm,
## neural} => {improv} 0.1333333 0.8000000 2.666667 4
## [14141] {train,
## improv,
## neural} => {perform} 0.1000000 0.7500000 1.607143 3
## [14142] {improv,
## neural,
## perform} => {train} 0.1000000 0.7500000 1.875000 3
## [14143] {train,
## improv,
## perform} => {neural} 0.1000000 0.7500000 2.250000 3
## [14144] {train,
## neural,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14145] {train,
## improv,
## neural} => {dataset} 0.1333333 1.0000000 2.307692 4
## [14146] {dataset,
## improv,
## neural} => {train} 0.1333333 0.8000000 2.000000 4
## [14147] {train,
## dataset,
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## [14148] {train,
## dataset,
## neural} => {improv} 0.1333333 0.8000000 2.666667 4
## [14149] {train,
## improv,
## neural} => {propos} 0.1000000 0.7500000 1.500000 3
## [14150] {improv,
## neural,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [14151] {train,
## improv,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [14152] {train,
## improv,
## neural} => {network} 0.1000000 0.7500000 1.184211 3
## [14153] {network,
## improv,
## neural} => {train} 0.1000000 0.7500000 1.875000 3
## [14154] {network,
## train,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [14155] {approach,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14156] {approach,
## dataset,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [14157] {approach,
## dataset,
## neural} => {improv} 0.1000000 0.7500000 2.500000 3
## [14158] {approach,
## improv,
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## [14159] {improv,
## neural,
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## [14160] {approach,
## improv,
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## [14161] {approach,
## improv,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [14162] {network,
## improv,
## neural} => {approach} 0.1000000 0.7500000 1.875000 3
## [14163] {approach,
## network,
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## [14164] {improv,
## neural,
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## [14165] {dataset,
## improv,
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## [14166] {dataset,
## improv,
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## [14167] {dataset,
## neural,
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## [14168] {improv,
## neural,
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## [14169] {improv,
## neural,
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## [14170] {improv,
## perform,
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## [14171] {neural,
## perform,
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## [14172] {improv,
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## [14173] {featur,
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## [14174] {featur,
## improv,
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## [14175] {featur,
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## [14176] {improv,
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## [14177] {network,
## improv,
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## [14178] {network,
## improv,
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## [14179] {network,
## neural,
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## [14180] {dataset,
## improv,
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## [14181] {improv,
## neural,
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## [14182] {dataset,
## improv,
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## [14183] {dataset,
## neural,
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## [14184] {model,
## improv,
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## [14185] {model,
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## [14186] {model,
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## [14187] {featur,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14188] {featur,
## dataset,
## improv} => {neural} 0.1000000 0.7500000 2.250000 3
## [14189] {featur,
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## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [14190] {dataset,
## improv,
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## [14191] {network,
## improv,
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## [14192] {network,
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## [14193] {network,
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## neural} => {improv} 0.1333333 0.8000000 2.666667 4
## [14194] {improv,
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## [14195] {model,
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## [14196] {model,
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## [14197] {model,
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## [14198] {improv,
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## [14199] {network,
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## [14200] {network,
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## [14201] {method,
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## [14202] {method,
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## [14203] {show,
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## [14204] {method,
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## [14205] {approach,
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## [14206] {method,
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## [14207] {approach,
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## [14208] {approach,
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## [14209] {approach,
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## [14210] {method,
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## [14211] {approach,
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## [14212] {approach,
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## [14213] {method,
## show,
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## [14214] {approach,
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## [14215] {method,
## improv,
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## [14216] {method,
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## [14217] {method,
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## [14218] {method,
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## [14219] {show,
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## [14220] {method,
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## [14221] {method,
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## [14222] {show,
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## [14223] {method,
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## [14224] {method,
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## [14225] {model,
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## [14226] {train,
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## [14227] {algorithm,
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## [14228] {train,
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## [14229] {train,
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## [14230] {train,
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## [14231] {network,
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## [14232] {network,
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## [14233] {network,
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## [14234] {approach,
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## [14235] {algorithm,
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## [14236] {approach,
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## [14237] {approach,
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## [14238] {approach,
## algorithm,
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## [14239] {algorithm,
## improv,
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## [14240] {approach,
## improv,
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## [14241] {approach,
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## [14242] {approach,
## algorithm,
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## [14243] {network,
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## [14244] {approach,
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## [14245] {approach,
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## [14246] {algorithm,
## improv,
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## [14247] {algorithm,
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## [14248] {algorithm,
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## [14249] {algorithm,
## improv,
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## [14250] {network,
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## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14251] {network,
## improv,
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## [14252] {network,
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## [14253] {algorithm,
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## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14254] {algorithm,
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## [14255] {dataset,
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## [14256] {algorithm,
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## [14257] {algorithm,
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## [14258] {network,
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## [14259] {network,
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## [14260] {network,
## algorithm,
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## [14261] {algorithm,
## improv,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [14262] {network,
## algorithm,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14263] {network,
## improv,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [14264] {network,
## algorithm,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [14265] {train,
## improv,
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## [14266] {train,
## improv,
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## [14267] {improv,
## perform,
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## [14268] {train,
## perform,
## work} => {improv} 0.1000000 1.0000000 3.333333 3
## [14269] {train,
## improv,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14270] {train,
## dataset,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14271] {train,
## dataset,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14272] {train,
## improv,
## perform} => {represent} 0.1000000 0.7500000 1.500000 3
## [14273] {represent,
## train,
## improv} => {perform} 0.1000000 1.0000000 2.142857 3
## [14274] {represent,
## improv,
## perform} => {train} 0.1000000 1.0000000 2.500000 3
## [14275] {represent,
## train,
## perform} => {improv} 0.1000000 1.0000000 3.333333 3
## [14276] {train,
## improv,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [14277] {train,
## improv,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [14278] {improv,
## perform,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [14279] {train,
## perform,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [14280] {train,
## dataset,
## improv} => {propos} 0.1000000 0.7500000 1.500000 3
## [14281] {train,
## improv,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [14282] {dataset,
## improv,
## propos} => {train} 0.1000000 0.7500000 1.875000 3
## [14283] {train,
## dataset,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [14284] {train,
## dataset,
## improv} => {network} 0.1000000 0.7500000 1.184211 3
## [14285] {network,
## train,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14286] {network,
## dataset,
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## [14287] {approach,
## improv,
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## [14288] {approach,
## dataset,
## improv} => {perform} 0.1000000 0.7500000 1.607143 3
## [14289] {approach,
## improv,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [14290] {approach,
## show,
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## [14291] {show,
## improv,
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## [14292] {approach,
## show,
## perform} => {improv} 0.1000000 0.7500000 2.500000 3
## [14293] {approach,
## dataset,
## improv} => {show} 0.1000000 0.7500000 1.406250 3
## [14294] {approach,
## show,
## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14295] {show,
## dataset,
## improv} => {approach} 0.1000000 1.0000000 2.500000 3
## [14296] {approach,
## dataset,
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## [14297] {approach,
## improv,
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## [14298] {dataset,
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## [14299] {approach,
## dataset,
## improv} => {model} 0.1000000 0.7500000 1.406250 3
## [14300] {approach,
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## improv} => {dataset} 0.1000000 1.0000000 2.307692 3
## [14301] {model,
## dataset,
## improv} => {approach} 0.1000000 0.7500000 1.875000 3
## [14302] {approach,
## dataset,
## improv} => {network} 0.1000000 0.7500000 1.184211 3
## [14303] {approach,
## network,
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## [14304] {network,
## dataset,
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## [14305] {approach,
## improv,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [14306] {approach,
## network,
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## [14307] {network,
## improv,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [14308] {improv,
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## [14309] {network,
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## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [16619] {featur,
## network,
## represent,
## captur} => {learn} 0.1000000 1.0000000 2.307692 3
## [16620] {featur,
## network,
## represent,
## learn} => {captur} 0.1000000 0.7500000 7.500000 3
## [16621] {approach,
## dataset,
## propos,
## paramet} => {network} 0.1000000 1.0000000 1.578947 3
## [16622] {approach,
## network,
## dataset,
## paramet} => {propos} 0.1000000 1.0000000 2.000000 3
## [16623] {approach,
## network,
## propos,
## paramet} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16624] {network,
## dataset,
## propos,
## paramet} => {approach} 0.1000000 1.0000000 2.500000 3
## [16625] {process,
## simpl,
## studi,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [16626] {network,
## process,
## simpl,
## studi} => {work} 0.1000000 1.0000000 2.500000 3
## [16627] {network,
## simpl,
## studi,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [16628] {network,
## process,
## simpl,
## work} => {studi} 0.1000000 1.0000000 7.500000 3
## [16629] {network,
## process,
## studi,
## work} => {simpl} 0.1000000 1.0000000 10.000000 3
## [16630] {train,
## dataset,
## result,
## increas} => {network} 0.1000000 1.0000000 1.578947 3
## [16631] {network,
## train,
## result,
## increas} => {dataset} 0.1000000 1.0000000 2.307692 3
## [16632] {network,
## dataset,
## result,
## increas} => {train} 0.1000000 1.0000000 2.500000 3
## [16633] {network,
## train,
## dataset,
## increas} => {result} 0.1000000 1.0000000 3.000000 3
## [16634] {network,
## train,
## dataset,
## result} => {increas} 0.1000000 0.7500000 5.625000 3
## [16635] {boltzmann,
## machin,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16636] {boltzmann,
## restrict,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16637] {boltzmann,
## machin,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16638] {boltzmann,
## machin,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16639] {machin,
## restrict,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16640] {boltzmann,
## machin,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16641] {boltzmann,
## restrict,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16642] {boltzmann,
## machin,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [16643] {boltzmann,
## machin,
## show,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16644] {machin,
## restrict,
## show,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16645] {boltzmann,
## machin,
## restrict,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16646] {boltzmann,
## model,
## restrict,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16647] {boltzmann,
## machin,
## model,
## restrict} => {recent} 0.1000000 0.7500000 3.214286 3
## [16648] {boltzmann,
## machin,
## model,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16649] {machin,
## model,
## restrict,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16650] {boltzmann,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16651] {boltzmann,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16652] {boltzmann,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16653] {boltzmann,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16654] {restrict,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16655] {boltzmann,
## restrict,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16656] {boltzmann,
## model,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16657] {boltzmann,
## model,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16658] {boltzmann,
## model,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16659] {model,
## restrict,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16660] {boltzmann,
## restrict,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16661] {boltzmann,
## model,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16662] {boltzmann,
## model,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [16663] {boltzmann,
## model,
## show,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16664] {model,
## restrict,
## show,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16665] {boltzmann,
## machin,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [16666] {boltzmann,
## machin,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16667] {boltzmann,
## restrict,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [16668] {boltzmann,
## machin,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16669] {machin,
## restrict,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16670] {boltzmann,
## machin,
## restrict,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [16671] {boltzmann,
## machin,
## model,
## restrict} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16672] {boltzmann,
## model,
## restrict,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [16673] {boltzmann,
## machin,
## model,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16674] {machin,
## model,
## restrict,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16675] {boltzmann,
## machin,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16676] {boltzmann,
## data,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16677] {boltzmann,
## data,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16678] {boltzmann,
## data,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16679] {data,
## machin,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16680] {boltzmann,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16681] {boltzmann,
## machin,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [16682] {boltzmann,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16683] {boltzmann,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16684] {machin,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16685] {boltzmann,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16686] {boltzmann,
## machin,
## model,
## restrict} => {task} 0.1000000 0.7500000 2.045455 3
## [16687] {boltzmann,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16688] {boltzmann,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16689] {machin,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16690] {boltzmann,
## machin,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16691] {boltzmann,
## featur,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16692] {boltzmann,
## featur,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16693] {boltzmann,
## featur,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16694] {featur,
## machin,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16695] {boltzmann,
## machin,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [16696] {boltzmann,
## machin,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [16697] {boltzmann,
## restrict,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [16698] {boltzmann,
## machin,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16699] {machin,
## restrict,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16700] {boltzmann,
## machin,
## restrict,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [16701] {boltzmann,
## machin,
## model,
## restrict} => {train} 0.1000000 0.7500000 1.875000 3
## [16702] {boltzmann,
## model,
## restrict,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [16703] {boltzmann,
## machin,
## model,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16704] {machin,
## model,
## restrict,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16705] {boltzmann,
## data,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16706] {boltzmann,
## machin,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [16707] {boltzmann,
## data,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16708] {boltzmann,
## data,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16709] {data,
## machin,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16710] {boltzmann,
## data,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [16711] {boltzmann,
## machin,
## model,
## restrict} => {data} 0.1000000 0.7500000 1.730769 3
## [16712] {boltzmann,
## data,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [16713] {boltzmann,
## data,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16714] {data,
## machin,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16715] {boltzmann,
## data,
## machin,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [16716] {boltzmann,
## featur,
## machin,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [16717] {boltzmann,
## data,
## featur,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [16718] {boltzmann,
## data,
## featur,
## machin} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16719] {data,
## featur,
## machin,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16720] {boltzmann,
## machin,
## restrict,
## show} => {model} 0.1333333 1.0000000 1.875000 4
## [16721] {boltzmann,
## machin,
## model,
## restrict} => {show} 0.1333333 1.0000000 1.875000 4
## [16722] {boltzmann,
## model,
## restrict,
## show} => {machin} 0.1333333 1.0000000 4.285714 4
## [16723] {boltzmann,
## machin,
## model,
## show} => {restrict} 0.1333333 1.0000000 7.500000 4
## [16724] {machin,
## model,
## restrict,
## show} => {boltzmann} 0.1333333 1.0000000 7.500000 4
## [16725] {boltzmann,
## machin,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16726] {boltzmann,
## featur,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16727] {boltzmann,
## featur,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16728] {boltzmann,
## featur,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16729] {featur,
## machin,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16730] {boltzmann,
## machin,
## model,
## restrict} => {featur} 0.1000000 0.7500000 1.406250 3
## [16731] {boltzmann,
## featur,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [16732] {boltzmann,
## featur,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [16733] {boltzmann,
## featur,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16734] {featur,
## machin,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16735] {boltzmann,
## restrict,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [16736] {boltzmann,
## model,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [16737] {boltzmann,
## model,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16738] {boltzmann,
## model,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16739] {model,
## restrict,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16740] {boltzmann,
## data,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16741] {boltzmann,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16742] {boltzmann,
## data,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16743] {boltzmann,
## data,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16744] {data,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16745] {boltzmann,
## data,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16746] {boltzmann,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16747] {boltzmann,
## data,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16748] {boltzmann,
## data,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16749] {data,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16750] {boltzmann,
## data,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16751] {boltzmann,
## featur,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16752] {boltzmann,
## data,
## featur,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16753] {boltzmann,
## data,
## featur,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16754] {data,
## featur,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16755] {boltzmann,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16756] {boltzmann,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16757] {boltzmann,
## model,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [16758] {boltzmann,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16759] {model,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16760] {boltzmann,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16761] {boltzmann,
## featur,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16762] {boltzmann,
## featur,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16763] {boltzmann,
## featur,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16764] {featur,
## restrict,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16765] {boltzmann,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16766] {boltzmann,
## featur,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16767] {boltzmann,
## featur,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16768] {boltzmann,
## featur,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16769] {featur,
## model,
## restrict,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16770] {boltzmann,
## restrict,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [16771] {boltzmann,
## model,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [16772] {boltzmann,
## model,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [16773] {boltzmann,
## model,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16774] {model,
## restrict,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16775] {boltzmann,
## data,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16776] {boltzmann,
## data,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16777] {boltzmann,
## model,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [16778] {boltzmann,
## data,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16779] {data,
## model,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16780] {boltzmann,
## data,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16781] {boltzmann,
## data,
## featur,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16782] {boltzmann,
## featur,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [16783] {boltzmann,
## data,
## featur,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16784] {data,
## featur,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16785] {boltzmann,
## data,
## model,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [16786] {boltzmann,
## data,
## featur,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [16787] {boltzmann,
## featur,
## model,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [16788] {boltzmann,
## data,
## featur,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16789] {data,
## featur,
## model,
## restrict} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16790] {boltzmann,
## model,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16791] {boltzmann,
## featur,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16792] {boltzmann,
## featur,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16793] {boltzmann,
## featur,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16794] {featur,
## model,
## restrict,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16795] {boltzmann,
## machin,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16796] {boltzmann,
## machin,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16797] {boltzmann,
## show,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16798] {boltzmann,
## machin,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16799] {machin,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16800] {boltzmann,
## machin,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16801] {boltzmann,
## machin,
## model,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16802] {boltzmann,
## model,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16803] {boltzmann,
## machin,
## model,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16804] {machin,
## model,
## algorithm,
## recent} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16805] {boltzmann,
## machin,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16806] {boltzmann,
## machin,
## model,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16807] {boltzmann,
## model,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16808] {boltzmann,
## machin,
## model,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [16809] {machin,
## model,
## show,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16810] {boltzmann,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16811] {boltzmann,
## model,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16812] {boltzmann,
## model,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16813] {boltzmann,
## model,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16814] {model,
## show,
## algorithm,
## recent} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16815] {boltzmann,
## machin,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [16816] {boltzmann,
## machin,
## model,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [16817] {boltzmann,
## machin,
## model,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16818] {boltzmann,
## model,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [16819] {machin,
## model,
## show,
## algorithm} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16820] {boltzmann,
## data,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16821] {boltzmann,
## machin,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16822] {boltzmann,
## data,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16823] {boltzmann,
## data,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16824] {data,
## machin,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16825] {boltzmann,
## data,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16826] {boltzmann,
## machin,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16827] {boltzmann,
## data,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [16828] {boltzmann,
## data,
## model,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16829] {data,
## machin,
## model,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16830] {boltzmann,
## data,
## machin,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16831] {boltzmann,
## featur,
## machin,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16832] {boltzmann,
## data,
## featur,
## machin} => {task} 0.1000000 1.0000000 2.727273 3
## [16833] {boltzmann,
## data,
## featur,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16834] {data,
## featur,
## machin,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16835] {boltzmann,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16836] {boltzmann,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16837] {boltzmann,
## machin,
## model,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [16838] {boltzmann,
## model,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16839] {machin,
## model,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16840] {boltzmann,
## machin,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16841] {boltzmann,
## featur,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16842] {boltzmann,
## featur,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16843] {boltzmann,
## featur,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16844] {featur,
## machin,
## show,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16845] {boltzmann,
## machin,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16846] {boltzmann,
## featur,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16847] {boltzmann,
## featur,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [16848] {boltzmann,
## featur,
## model,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16849] {featur,
## machin,
## model,
## task} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16850] {boltzmann,
## machin,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [16851] {boltzmann,
## machin,
## model,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [16852] {boltzmann,
## machin,
## model,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [16853] {boltzmann,
## model,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [16854] {machin,
## model,
## show,
## train} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16855] {boltzmann,
## data,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16856] {boltzmann,
## data,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [16857] {boltzmann,
## machin,
## model,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [16858] {boltzmann,
## data,
## model,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16859] {data,
## machin,
## model,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16860] {boltzmann,
## data,
## machin,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16861] {boltzmann,
## data,
## featur,
## machin} => {show} 0.1000000 1.0000000 1.875000 3
## [16862] {boltzmann,
## featur,
## machin,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [16863] {boltzmann,
## data,
## featur,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16864] {data,
## featur,
## machin,
## show} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16865] {boltzmann,
## data,
## machin,
## model} => {featur} 0.1000000 1.0000000 1.875000 3
## [16866] {boltzmann,
## data,
## featur,
## machin} => {model} 0.1000000 1.0000000 1.875000 3
## [16867] {boltzmann,
## featur,
## machin,
## model} => {data} 0.1000000 1.0000000 2.307692 3
## [16868] {boltzmann,
## data,
## featur,
## model} => {machin} 0.1000000 1.0000000 4.285714 3
## [16869] {data,
## featur,
## machin,
## model} => {boltzmann} 0.1000000 1.0000000 7.500000 3
## [16870] {boltzmann,
## machin,
## model,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16871] {boltzmann,
## featur,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16872] {boltzmann,
## featur,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [16873] {boltzmann,
## featur,
## model,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16874] {featur,
## machin,
## model,
## show} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16875] {boltzmann,
## data,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16876] {boltzmann,
## data,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16877] {boltzmann,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16878] {boltzmann,
## data,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16879] {data,
## model,
## show,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16880] {boltzmann,
## data,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16881] {boltzmann,
## data,
## featur,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16882] {boltzmann,
## featur,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16883] {boltzmann,
## data,
## featur,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16884] {boltzmann,
## data,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16885] {boltzmann,
## data,
## featur,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16886] {boltzmann,
## featur,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16887] {boltzmann,
## data,
## featur,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [16888] {boltzmann,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16889] {boltzmann,
## featur,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16890] {boltzmann,
## featur,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16891] {boltzmann,
## featur,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16892] {featur,
## model,
## show,
## task} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16893] {boltzmann,
## data,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16894] {boltzmann,
## data,
## featur,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16895] {boltzmann,
## data,
## featur,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [16896] {boltzmann,
## featur,
## model,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [16897] {data,
## featur,
## model,
## show} => {boltzmann} 0.1000000 0.7500000 5.625000 3
## [16898] {machin,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16899] {machin,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16900] {restrict,
## show,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16901] {machin,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16902] {machin,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16903] {machin,
## restrict,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16904] {machin,
## model,
## restrict,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16905] {model,
## restrict,
## algorithm,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16906] {machin,
## model,
## restrict,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16907] {machin,
## model,
## algorithm,
## recent} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16908] {machin,
## restrict,
## show,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16909] {machin,
## model,
## restrict,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16910] {model,
## restrict,
## show,
## recent} => {machin} 0.1000000 1.0000000 4.285714 3
## [16911] {machin,
## model,
## restrict,
## show} => {recent} 0.1000000 0.7500000 3.214286 3
## [16912] {machin,
## model,
## show,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16913] {restrict,
## show,
## algorithm,
## recent} => {model} 0.1000000 1.0000000 1.875000 3
## [16914] {model,
## restrict,
## algorithm,
## recent} => {show} 0.1000000 1.0000000 1.875000 3
## [16915] {model,
## restrict,
## show,
## recent} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [16916] {model,
## restrict,
## show,
## algorithm} => {recent} 0.1000000 1.0000000 4.285714 3
## [16917] {model,
## show,
## algorithm,
## recent} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16918] {machin,
## restrict,
## show,
## algorithm} => {model} 0.1000000 1.0000000 1.875000 3
## [16919] {machin,
## model,
## restrict,
## algorithm} => {show} 0.1000000 1.0000000 1.875000 3
## [16920] {machin,
## model,
## restrict,
## show} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [16921] {model,
## restrict,
## show,
## algorithm} => {machin} 0.1000000 1.0000000 4.285714 3
## [16922] {machin,
## model,
## show,
## algorithm} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16923] {data,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16924] {machin,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16925] {data,
## machin,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16926] {data,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16927] {data,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16928] {data,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16929] {machin,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16930] {data,
## machin,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16931] {data,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16932] {data,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16933] {data,
## machin,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16934] {featur,
## machin,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16935] {data,
## featur,
## machin,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16936] {data,
## featur,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16937] {data,
## featur,
## machin,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16938] {machin,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16939] {machin,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16940] {machin,
## model,
## restrict,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [16941] {model,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16942] {machin,
## model,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16943] {machin,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16944] {featur,
## machin,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16945] {featur,
## machin,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16946] {featur,
## restrict,
## show,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16947] {featur,
## machin,
## show,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16948] {machin,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16949] {featur,
## machin,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16950] {featur,
## machin,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16951] {featur,
## model,
## restrict,
## task} => {machin} 0.1000000 1.0000000 4.285714 3
## [16952] {featur,
## machin,
## model,
## task} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16953] {machin,
## restrict,
## show,
## train} => {model} 0.1000000 1.0000000 1.875000 3
## [16954] {machin,
## model,
## restrict,
## train} => {show} 0.1000000 1.0000000 1.875000 3
## [16955] {machin,
## model,
## restrict,
## show} => {train} 0.1000000 0.7500000 1.875000 3
## [16956] {model,
## restrict,
## show,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [16957] {machin,
## model,
## show,
## train} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16958] {data,
## machin,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16959] {data,
## machin,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16960] {machin,
## model,
## restrict,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [16961] {data,
## model,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16962] {data,
## machin,
## model,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16963] {data,
## machin,
## restrict,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [16964] {data,
## featur,
## machin,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16965] {featur,
## machin,
## restrict,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [16966] {data,
## featur,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16967] {data,
## featur,
## machin,
## show} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16968] {data,
## machin,
## model,
## restrict} => {featur} 0.1000000 1.0000000 1.875000 3
## [16969] {data,
## featur,
## machin,
## restrict} => {model} 0.1000000 1.0000000 1.875000 3
## [16970] {featur,
## machin,
## model,
## restrict} => {data} 0.1000000 1.0000000 2.307692 3
## [16971] {data,
## featur,
## model,
## restrict} => {machin} 0.1000000 1.0000000 4.285714 3
## [16972] {data,
## featur,
## machin,
## model} => {restrict} 0.1000000 1.0000000 7.500000 3
## [16973] {machin,
## model,
## restrict,
## show} => {featur} 0.1000000 0.7500000 1.406250 3
## [16974] {featur,
## machin,
## restrict,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [16975] {featur,
## machin,
## model,
## restrict} => {show} 0.1000000 1.0000000 1.875000 3
## [16976] {featur,
## model,
## restrict,
## show} => {machin} 0.1000000 1.0000000 4.285714 3
## [16977] {featur,
## machin,
## model,
## show} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16978] {data,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16979] {data,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16980] {model,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16981] {data,
## model,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16982] {data,
## model,
## show,
## task} => {restrict} 0.1000000 0.7500000 5.625000 3
## [16983] {data,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16984] {data,
## featur,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16985] {featur,
## restrict,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16986] {data,
## featur,
## restrict,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [16987] {data,
## model,
## restrict,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16988] {data,
## featur,
## restrict,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16989] {featur,
## model,
## restrict,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [16990] {data,
## featur,
## model,
## restrict} => {task} 0.1000000 1.0000000 2.727273 3
## [16991] {model,
## restrict,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [16992] {featur,
## restrict,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [16993] {featur,
## model,
## restrict,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [16994] {featur,
## model,
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## [19301] {appli,
## architectur,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19302] {network,
## appli,
## architectur,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19303] {network,
## appli,
## architectur,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19304] {network,
## appli,
## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [19305] {network,
## architectur,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19306] {appli,
## architectur,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19307] {network,
## appli,
## architectur,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19308] {network,
## appli,
## architectur,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19309] {network,
## appli,
## dataset,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [19310] {network,
## architectur,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19311] {classif,
## method,
## appli,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [19312] {classif,
## method,
## show,
## appli} => {perform} 0.1000000 1.0000000 2.142857 3
## [19313] {classif,
## show,
## appli,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [19314] {method,
## show,
## appli,
## perform} => {classif} 0.1000000 1.0000000 3.750000 3
## [19315] {classif,
## method,
## show,
## perform} => {appli} 0.1000000 0.7500000 3.750000 3
## [19316] {classif,
## method,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19317] {classif,
## method,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19318] {classif,
## appli,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19319] {method,
## appli,
## perform,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [19320] {classif,
## method,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19321] {classif,
## method,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [19322] {classif,
## method,
## appli,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19323] {classif,
## show,
## appli,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19324] {method,
## show,
## appli,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [19325] {classif,
## method,
## show,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19326] {classif,
## show,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19327] {classif,
## appli,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19328] {classif,
## show,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19329] {show,
## appli,
## perform,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [19330] {classif,
## show,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19331] {approach,
## appli,
## dataset,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [19332] {approach,
## show,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19333] {show,
## appli,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [19334] {approach,
## show,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [19335] {approach,
## show,
## dataset,
## neural} => {appli} 0.1000000 1.0000000 5.000000 3
## [19336] {approach,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19337] {approach,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19338] {appli,
## dataset,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19339] {approach,
## appli,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19340] {approach,
## dataset,
## neural,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19341] {approach,
## appli,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [19342] {approach,
## network,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19343] {network,
## appli,
## dataset,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [19344] {approach,
## network,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [19345] {approach,
## network,
## dataset,
## neural} => {appli} 0.1000000 0.7500000 3.750000 3
## [19346] {approach,
## show,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19347] {approach,
## appli,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19348] {show,
## appli,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19349] {approach,
## show,
## appli,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19350] {approach,
## show,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19351] {approach,
## show,
## appli,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [19352] {approach,
## network,
## appli,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [19353] {network,
## show,
## appli,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [19354] {approach,
## network,
## show,
## appli} => {neural} 0.1000000 1.0000000 3.000000 3
## [19355] {approach,
## network,
## show,
## neural} => {appli} 0.1000000 0.7500000 3.750000 3
## [19356] {approach,
## appli,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19357] {approach,
## network,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19358] {network,
## appli,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19359] {approach,
## network,
## appli,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19360] {show,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19361] {appli,
## dataset,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19362] {show,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19363] {show,
## appli,
## dataset,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19364] {show,
## dataset,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19365] {show,
## appli,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [19366] {network,
## appli,
## dataset,
## neural} => {show} 0.1000000 1.0000000 1.875000 3
## [19367] {network,
## show,
## appli,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19368] {network,
## show,
## appli,
## dataset} => {neural} 0.1000000 1.0000000 3.000000 3
## [19369] {network,
## show,
## dataset,
## neural} => {appli} 0.1000000 1.0000000 5.000000 3
## [19370] {appli,
## dataset,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19371] {network,
## appli,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19372] {network,
## appli,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19373] {network,
## appli,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [19374] {network,
## dataset,
## neural,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19375] {show,
## appli,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19376] {network,
## show,
## appli,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [19377] {network,
## appli,
## neural,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19378] {network,
## show,
## appli,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [19379] {network,
## show,
## neural,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19380] {method,
## appli,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19381] {method,
## appli,
## perform,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [19382] {method,
## appli,
## dataset,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19383] {appli,
## dataset,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19384] {method,
## dataset,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19385] {method,
## appli,
## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [19386] {method,
## network,
## appli,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19387] {method,
## network,
## appli,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [19388] {network,
## appli,
## dataset,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [19389] {method,
## network,
## dataset,
## perform} => {appli} 0.1000000 1.0000000 5.000000 3
## [19390] {method,
## show,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19391] {method,
## appli,
## perform,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [19392] {method,
## show,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19393] {show,
## appli,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19394] {method,
## show,
## perform,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19395] {method,
## appli,
## perform,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [19396] {featur,
## method,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19397] {featur,
## method,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19398] {featur,
## appli,
## perform,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [19399] {featur,
## method,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19400] {method,
## appli,
## perform,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [19401] {method,
## network,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19402] {method,
## network,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19403] {network,
## appli,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [19404] {method,
## network,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19405] {method,
## appli,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19406] {method,
## network,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19407] {method,
## network,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19408] {network,
## appli,
## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [19409] {method,
## network,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19410] {approach,
## algorithm,
## appli,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19411] {approach,
## algorithm,
## appli,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [19412] {algorithm,
## appli,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19413] {approach,
## appli,
## perform,
## propos} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [19414] {approach,
## algorithm,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19415] {approach,
## show,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19416] {approach,
## appli,
## dataset,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19417] {approach,
## show,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19418] {show,
## appli,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19419] {approach,
## show,
## appli,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [19420] {approach,
## network,
## appli,
## dataset} => {show} 0.1000000 1.0000000 1.875000 3
## [19421] {approach,
## network,
## show,
## appli} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19422] {network,
## show,
## appli,
## dataset} => {approach} 0.1000000 1.0000000 2.500000 3
## [19423] {approach,
## network,
## show,
## dataset} => {appli} 0.1000000 0.7500000 3.750000 3
## [19424] {approach,
## appli,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19425] {approach,
## network,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19426] {approach,
## network,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19427] {network,
## appli,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [19428] {approach,
## show,
## appli,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19429] {approach,
## network,
## show,
## appli} => {propos} 0.1000000 1.0000000 2.000000 3
## [19430] {approach,
## network,
## appli,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [19431] {network,
## show,
## appli,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [19432] {approach,
## network,
## show,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19433] {appli,
## dataset,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [19434] {network,
## appli,
## dataset,
## work} => {propos} 0.1000000 1.0000000 2.000000 3
## [19435] {network,
## appli,
## propos,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19436] {network,
## appli,
## dataset,
## propos} => {work} 0.1000000 0.7500000 1.875000 3
## [19437] {appli,
## dataset,
## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19438] {network,
## appli,
## dataset,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [19439] {network,
## appli,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19440] {network,
## appli,
## dataset,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [19441] {network,
## dataset,
## perform,
## propos} => {appli} 0.1000000 1.0000000 5.000000 3
## [19442] {show,
## appli,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [19443] {network,
## show,
## appli,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [19444] {network,
## appli,
## dataset,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [19445] {network,
## show,
## appli,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19446] {network,
## show,
## dataset,
## propos} => {appli} 0.1000000 0.7500000 3.750000 3
## [19447] {data,
## dataset,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [19448] {data,
## represent,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19449] {represent,
## dataset,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [19450] {data,
## represent,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [19451] {data,
## represent,
## dataset,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [19452] {data,
## dataset,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [19453] {data,
## featur,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19454] {featur,
## dataset,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [19455] {data,
## featur,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [19456] {data,
## featur,
## dataset,
## work} => {larg} 0.1000000 0.7500000 4.500000 3
## [19457] {data,
## represent,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [19458] {data,
## featur,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [19459] {featur,
## represent,
## work,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [19460] {data,
## featur,
## represent,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [19461] {data,
## featur,
## represent,
## work} => {larg} 0.1000000 1.0000000 6.000000 3
## [19462] {represent,
## dataset,
## work,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [19463] {featur,
## dataset,
## work,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [19464] {featur,
## represent,
## work,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19465] {featur,
## represent,
## dataset,
## larg} => {work} 0.1000000 1.0000000 2.500000 3
## [19466] {featur,
## represent,
## dataset,
## work} => {larg} 0.1000000 1.0000000 6.000000 3
## [19467] {data,
## represent,
## dataset,
## larg} => {featur} 0.1000000 1.0000000 1.875000 3
## [19468] {data,
## featur,
## dataset,
## larg} => {represent} 0.1000000 1.0000000 2.000000 3
## [19469] {data,
## featur,
## represent,
## larg} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19470] {featur,
## represent,
## dataset,
## larg} => {data} 0.1000000 1.0000000 2.307692 3
## [19471] {data,
## achiev,
## dataset,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19472] {data,
## achiev,
## learn,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19473] {achiev,
## dataset,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19474] {data,
## dataset,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19475] {data,
## achiev,
## dataset,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19476] {data,
## achiev,
## dataset,
## challeng} => {represent} 0.1000000 1.0000000 2.000000 3
## [19477] {data,
## represent,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19478] {represent,
## achiev,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19479] {data,
## represent,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19480] {data,
## represent,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19481] {data,
## achiev,
## dataset,
## challeng} => {featur} 0.1000000 1.0000000 1.875000 3
## [19482] {data,
## featur,
## achiev,
## challeng} => {dataset} 0.1000000 1.0000000 2.307692 3
## [19483] {featur,
## achiev,
## dataset,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19484] {data,
## featur,
## dataset,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19485] {data,
## featur,
## achiev,
## dataset} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19486] {data,
## achiev,
## learn,
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## [19487] {data,
## represent,
## achiev,
## challeng} => {learn} 0.1000000 1.0000000 2.307692 3
## [19488] {represent,
## achiev,
## learn,
## challeng} => {data} 0.1000000 1.0000000 2.307692 3
## [19489] {data,
## represent,
## learn,
## challeng} => {achiev} 0.1000000 1.0000000 4.285714 3
## [19490] {data,
## represent,
## achiev,
## learn} => {challeng} 0.1000000 1.0000000 6.000000 3
## [19491] {data,
## achiev,
## learn,
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## [19688] {featur,
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## [19689] {featur,
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## [19700] {paper,
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## [19701] {paper,
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## [20078] {approach,
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## [20079] {approach,
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## [20080] {approach,
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## [20081] {approach,
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## [20082] {input,
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## [20083] {approach,
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## [20084] {approach,
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## [20085] {approach,
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## [20088] {approach,
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## [20090] {approach,
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## [20096] {approach,
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## [20097] {approach,
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## [20098] {input,
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## [20099] {approach,
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## [20100] {approach,
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## [20107] {approach,
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## [20108] {input,
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## [20109] {approach,
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## [20110] {approach,
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## [20111] {approach,
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## [20112] {approach,
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## [20113] {input,
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## [20114] {approach,
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## [20115] {approach,
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## [20116] {approach,
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## [20117] {approach,
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## [20118] {input,
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## [20119] {approach,
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## [20120] {approach,
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## [20121] {approach,
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## [20122] {approach,
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## [20123] {featur,
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## [20130] {reduc,
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## [20131] {reduc,
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## [20132] {improv,
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## [20133] {reduc,
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## [20138] {machin,
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## [20139] {machin,
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## [20140] {machin,
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## [20141] {machin,
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## [20143] {machin,
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## [20144] {machin,
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## [20145] {machin,
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## [20146] {machin,
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## [20147] {machin,
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## [20148] {machin,
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## [20149] {featur,
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## [20157] {featur,
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## [20160] {featur,
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## [20197] {featur,
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## [20198] {featur,
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## [20203] {featur,
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## [20204] {featur,
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## [20205] {featur,
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## [20208] {featur,
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## [20209] {featur,
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## [20264] {classif,
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## [20265] {approach,
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## [20266] {approach,
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## [20267] {approach,
## classif,
## featur,
## method} => {machin} 0.1000000 0.7500000 3.214286 3
## [20268] {classif,
## machin,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [20269] {classif,
## featur,
## machin,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [20270] {classif,
## featur,
## machin,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [20271] {featur,
## machin,
## task,
## train} => {classif} 0.1000000 1.0000000 3.750000 3
## [20272] {classif,
## featur,
## task,
## train} => {machin} 0.1000000 1.0000000 4.285714 3
## [20273] {classif,
## machin,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20274] {classif,
## featur,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20275] {classif,
## featur,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [20276] {featur,
## machin,
## model,
## show} => {classif} 0.1000000 0.7500000 2.812500 3
## [20277] {classif,
## featur,
## model,
## show} => {machin} 0.1000000 0.7500000 3.214286 3
## [20278] {machin,
## paper,
## task,
## train} => {featur} 0.1000000 1.0000000 1.875000 3
## [20279] {featur,
## machin,
## paper,
## task} => {train} 0.1000000 1.0000000 2.500000 3
## [20280] {featur,
## machin,
## paper,
## train} => {task} 0.1000000 1.0000000 2.727273 3
## [20281] {featur,
## machin,
## task,
## train} => {paper} 0.1000000 1.0000000 3.000000 3
## [20282] {featur,
## paper,
## task,
## train} => {machin} 0.1000000 0.7500000 3.214286 3
## [20283] {data,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [20284] {data,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20285] {machin,
## model,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20286] {data,
## machin,
## model,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20287] {data,
## model,
## show,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [20288] {data,
## machin,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20289] {data,
## featur,
## machin,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20290] {featur,
## machin,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20291] {data,
## featur,
## machin,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20292] {data,
## machin,
## model,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20293] {data,
## featur,
## machin,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [20294] {featur,
## machin,
## model,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20295] {data,
## featur,
## machin,
## model} => {task} 0.1000000 1.0000000 2.727273 3
## [20296] {machin,
## model,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20297] {featur,
## machin,
## show,
## task} => {model} 0.1000000 1.0000000 1.875000 3
## [20298] {featur,
## machin,
## model,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20299] {featur,
## machin,
## model,
## show} => {task} 0.1000000 0.7500000 2.045455 3
## [20300] {featur,
## model,
## show,
## task} => {machin} 0.1000000 0.7500000 3.214286 3
## [20301] {data,
## machin,
## model,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20302] {data,
## featur,
## machin,
## show} => {model} 0.1000000 1.0000000 1.875000 3
## [20303] {data,
## featur,
## machin,
## model} => {show} 0.1000000 1.0000000 1.875000 3
## [20304] {featur,
## machin,
## model,
## show} => {data} 0.1000000 0.7500000 1.730769 3
## [20305] {data,
## featur,
## model,
## show} => {machin} 0.1000000 0.7500000 3.214286 3
## [20306] {machin,
## model,
## show,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20307] {featur,
## machin,
## show,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [20308] {featur,
## machin,
## model,
## learn} => {show} 0.1000000 1.0000000 1.875000 3
## [20309] {featur,
## machin,
## model,
## show} => {learn} 0.1000000 0.7500000 1.730769 3
## [20310] {classif,
## architectur,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20311] {architectur,
## dataset,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [20312] {classif,
## dataset,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20313] {classif,
## architectur,
## dataset,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [20314] {classif,
## architectur,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [20315] {classif,
## architectur,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20316] {architectur,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20317] {classif,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20318] {classif,
## architectur,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20319] {classif,
## architectur,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20320] {classif,
## architectur,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20321] {network,
## architectur,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [20322] {classif,
## network,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20323] {classif,
## network,
## architectur,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [20324] {classif,
## network,
## architectur,
## experi} => {process} 0.1000000 0.7500000 3.750000 3
## [20325] {architectur,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20326] {architectur,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20327] {dataset,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20328] {architectur,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20329] {architectur,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20330] {architectur,
## dataset,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20331] {network,
## architectur,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20332] {network,
## dataset,
## experi,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20333] {network,
## architectur,
## dataset,
## process} => {experi} 0.1000000 0.7500000 2.812500 3
## [20334] {network,
## architectur,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [20335] {architectur,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20336] {network,
## architectur,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20337] {network,
## experi,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20338] {network,
## architectur,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20339] {network,
## architectur,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20340] {classif,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20341] {classif,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20342] {dataset,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20343] {classif,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20344] {classif,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20345] {classif,
## dataset,
## experi,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20346] {classif,
## network,
## experi,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20347] {network,
## dataset,
## experi,
## process} => {classif} 0.1000000 1.0000000 3.750000 3
## [20348] {classif,
## network,
## dataset,
## process} => {experi} 0.1000000 1.0000000 3.750000 3
## [20349] {classif,
## network,
## dataset,
## experi} => {process} 0.1000000 1.0000000 5.000000 3
## [20350] {classif,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20351] {classif,
## network,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20352] {network,
## experi,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20353] {classif,
## network,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20354] {classif,
## network,
## experi,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20355] {dataset,
## experi,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20356] {network,
## dataset,
## experi,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20357] {network,
## experi,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20358] {network,
## dataset,
## process,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [20359] {network,
## dataset,
## experi,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20360] {classif,
## architectur,
## dataset,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20361] {classif,
## architectur,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20362] {architectur,
## dataset,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20363] {classif,
## dataset,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20364] {classif,
## architectur,
## dataset,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20365] {classif,
## architectur,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20366] {classif,
## network,
## architectur,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20367] {network,
## architectur,
## dataset,
## process} => {classif} 0.1000000 0.7500000 2.812500 3
## [20368] {classif,
## network,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20369] {classif,
## network,
## architectur,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [20370] {classif,
## architectur,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20371] {classif,
## network,
## architectur,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20372] {network,
## architectur,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20373] {classif,
## network,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20374] {classif,
## network,
## architectur,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20375] {model,
## architectur,
## process,
## recognit} => {featur} 0.1000000 1.0000000 1.875000 3
## [20376] {featur,
## architectur,
## process,
## recognit} => {model} 0.1000000 1.0000000 1.875000 3
## [20377] {featur,
## model,
## architectur,
## process} => {recognit} 0.1000000 1.0000000 3.333333 3
## [20378] {featur,
## model,
## process,
## recognit} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20379] {featur,
## model,
## architectur,
## recognit} => {process} 0.1000000 1.0000000 5.000000 3
## [20380] {architectur,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20381] {algorithm,
## architectur,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20382] {algorithm,
## architectur,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20383] {algorithm,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20384] {algorithm,
## architectur,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20385] {architectur,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20386] {architectur,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20387] {architectur,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20388] {improv,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20389] {architectur,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20390] {architectur,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20391] {architectur,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20392] {architectur,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20393] {dataset,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20394] {architectur,
## dataset,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20395] {architectur,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20396] {network,
## architectur,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20397] {network,
## architectur,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20398] {network,
## improv,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20399] {network,
## architectur,
## improv,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20400] {algorithm,
## architectur,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20401] {architectur,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20402] {algorithm,
## architectur,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20403] {algorithm,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20404] {algorithm,
## architectur,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20405] {algorithm,
## architectur,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20406] {architectur,
## dataset,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20407] {algorithm,
## architectur,
## dataset,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20408] {algorithm,
## dataset,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20409] {algorithm,
## architectur,
## dataset,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [20410] {algorithm,
## architectur,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20411] {network,
## architectur,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20412] {network,
## algorithm,
## architectur,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20413] {network,
## algorithm,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20414] {network,
## algorithm,
## architectur,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [20415] {architectur,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20416] {architectur,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20417] {architectur,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20418] {dataset,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20419] {architectur,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20420] {architectur,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20421] {network,
## architectur,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20422] {network,
## architectur,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20423] {network,
## improv,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20424] {network,
## architectur,
## improv,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [20425] {architectur,
## dataset,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20426] {network,
## architectur,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20427] {network,
## architectur,
## dataset,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [20428] {network,
## dataset,
## improv,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20429] {network,
## architectur,
## dataset,
## improv} => {process} 0.1000000 1.0000000 5.000000 3
## [20430] {algorithm,
## architectur,
## process,
## result} => {featur} 0.1000000 1.0000000 1.875000 3
## [20431] {featur,
## architectur,
## process,
## result} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20432] {featur,
## algorithm,
## architectur,
## process} => {result} 0.1000000 1.0000000 3.000000 3
## [20433] {featur,
## algorithm,
## process,
## result} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20434] {featur,
## algorithm,
## architectur,
## result} => {process} 0.1000000 1.0000000 5.000000 3
## [20435] {algorithm,
## architectur,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20436] {architectur,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20437] {algorithm,
## architectur,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20438] {algorithm,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20439] {algorithm,
## architectur,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20440] {algorithm,
## architectur,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20441] {architectur,
## dataset,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20442] {algorithm,
## architectur,
## dataset,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20443] {algorithm,
## dataset,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20444] {algorithm,
## architectur,
## dataset,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20445] {algorithm,
## architectur,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20446] {network,
## architectur,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20447] {network,
## algorithm,
## architectur,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20448] {network,
## algorithm,
## neural,
## process} => {architectur} 0.1000000 0.7500000 2.812500 3
## [20449] {network,
## algorithm,
## architectur,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20450] {architectur,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20451] {architectur,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20452] {architectur,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20453] {dataset,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20454] {architectur,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20455] {architectur,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20456] {network,
## architectur,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20457] {network,
## architectur,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20458] {network,
## neural,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20459] {network,
## architectur,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20460] {architectur,
## dataset,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20461] {network,
## architectur,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20462] {network,
## architectur,
## dataset,
## process} => {neural} 0.1000000 0.7500000 2.250000 3
## [20463] {network,
## dataset,
## neural,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20464] {network,
## architectur,
## dataset,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20465] {algorithm,
## architectur,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20466] {algorithm,
## architectur,
## dataset,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20467] {architectur,
## dataset,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20468] {algorithm,
## dataset,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20469] {algorithm,
## architectur,
## dataset,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20470] {algorithm,
## architectur,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20471] {network,
## algorithm,
## architectur,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20472] {network,
## architectur,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20473] {network,
## algorithm,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20474] {network,
## algorithm,
## architectur,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20475] {algorithm,
## architectur,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20476] {network,
## algorithm,
## architectur,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20477] {network,
## architectur,
## dataset,
## process} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [20478] {network,
## algorithm,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20479] {network,
## algorithm,
## architectur,
## dataset} => {process} 0.1000000 1.0000000 5.000000 3
## [20480] {architectur,
## dataset,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [20481] {network,
## architectur,
## process,
## work} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20482] {network,
## architectur,
## dataset,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [20483] {network,
## dataset,
## process,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20484] {network,
## architectur,
## dataset,
## work} => {process} 0.1000000 0.7500000 3.750000 3
## [20485] {architectur,
## dataset,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20486] {network,
## architectur,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20487] {network,
## architectur,
## dataset,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [20488] {network,
## dataset,
## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20489] {network,
## architectur,
## dataset,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [20490] {architectur,
## dataset,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20491] {network,
## architectur,
## dataset,
## process} => {propos} 0.1000000 0.7500000 1.500000 3
## [20492] {network,
## architectur,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20493] {network,
## dataset,
## process,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20494] {network,
## architectur,
## dataset,
## propos} => {process} 0.1000000 0.7500000 3.750000 3
## [20495] {featur,
## architectur,
## dataset,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20496] {network,
## architectur,
## dataset,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [20497] {featur,
## network,
## architectur,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20498] {featur,
## network,
## dataset,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [20499] {featur,
## network,
## architectur,
## dataset} => {process} 0.1000000 0.7500000 3.750000 3
## [20500] {classif,
## dataset,
## process,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20501] {classif,
## network,
## dataset,
## process} => {propos} 0.1000000 1.0000000 2.000000 3
## [20502] {classif,
## network,
## process,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20503] {network,
## dataset,
## process,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [20504] {classif,
## network,
## dataset,
## propos} => {process} 0.1000000 1.0000000 5.000000 3
## [20505] {algorithm,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20506] {improv,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20507] {algorithm,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20508] {algorithm,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20509] {algorithm,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20510] {algorithm,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20511] {dataset,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20512] {algorithm,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20513] {algorithm,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20514] {algorithm,
## dataset,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20515] {algorithm,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20516] {network,
## improv,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20517] {network,
## algorithm,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20518] {network,
## algorithm,
## neural,
## process} => {improv} 0.1000000 0.7500000 2.500000 3
## [20519] {network,
## algorithm,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20520] {improv,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20521] {dataset,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20522] {dataset,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20523] {dataset,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20524] {dataset,
## improv,
## neural,
## perform} => {process} 0.1000000 0.7500000 3.750000 3
## [20525] {improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20526] {network,
## improv,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20527] {network,
## improv,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20528] {network,
## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20529] {network,
## improv,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20530] {dataset,
## improv,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20531] {network,
## improv,
## neural,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20532] {network,
## dataset,
## improv,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20533] {network,
## dataset,
## neural,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20534] {network,
## dataset,
## improv,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20535] {algorithm,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20536] {algorithm,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20537] {dataset,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20538] {algorithm,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20539] {algorithm,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20540] {algorithm,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20541] {network,
## algorithm,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20542] {network,
## improv,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20543] {network,
## algorithm,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20544] {network,
## algorithm,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20545] {algorithm,
## dataset,
## improv,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20546] {network,
## algorithm,
## improv,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20547] {network,
## dataset,
## improv,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20548] {network,
## algorithm,
## dataset,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20549] {network,
## algorithm,
## dataset,
## improv} => {process} 0.1000000 0.7500000 3.750000 3
## [20550] {dataset,
## improv,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20551] {network,
## improv,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20552] {network,
## dataset,
## improv,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20553] {network,
## dataset,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [20554] {network,
## dataset,
## improv,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20555] {approach,
## algorithm,
## neural,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [20556] {show,
## algorithm,
## neural,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [20557] {approach,
## show,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20558] {approach,
## show,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20559] {approach,
## show,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20560] {approach,
## algorithm,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20561] {network,
## algorithm,
## neural,
## process} => {approach} 0.1000000 0.7500000 1.875000 3
## [20562] {approach,
## network,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20563] {approach,
## network,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20564] {approach,
## network,
## algorithm,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20565] {algorithm,
## neural,
## process,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [20566] {network,
## algorithm,
## neural,
## process} => {work} 0.1000000 0.7500000 1.875000 3
## [20567] {network,
## neural,
## process,
## work} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20568] {network,
## algorithm,
## process,
## work} => {neural} 0.1000000 1.0000000 3.000000 3
## [20569] {network,
## algorithm,
## neural,
## work} => {process} 0.1000000 1.0000000 5.000000 3
## [20570] {algorithm,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20571] {algorithm,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20572] {dataset,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20573] {algorithm,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20574] {algorithm,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20575] {algorithm,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20576] {network,
## algorithm,
## neural,
## process} => {perform} 0.1000000 0.7500000 1.607143 3
## [20577] {network,
## neural,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20578] {network,
## algorithm,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20579] {network,
## algorithm,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20580] {algorithm,
## dataset,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20581] {network,
## algorithm,
## neural,
## process} => {dataset} 0.1000000 0.7500000 1.730769 3
## [20582] {network,
## dataset,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20583] {network,
## algorithm,
## dataset,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20584] {network,
## algorithm,
## dataset,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20585] {show,
## algorithm,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20586] {network,
## algorithm,
## neural,
## process} => {show} 0.1000000 0.7500000 1.406250 3
## [20587] {network,
## show,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20588] {network,
## show,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20589] {network,
## show,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20590] {featur,
## algorithm,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20591] {network,
## algorithm,
## neural,
## process} => {featur} 0.1000000 0.7500000 1.406250 3
## [20592] {featur,
## network,
## neural,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20593] {featur,
## network,
## algorithm,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20594] {featur,
## network,
## algorithm,
## neural} => {process} 0.1000000 1.0000000 5.000000 3
## [20595] {approach,
## show,
## neural,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20596] {approach,
## network,
## neural,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [20597] {network,
## show,
## neural,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [20598] {approach,
## network,
## show,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20599] {approach,
## network,
## show,
## neural} => {process} 0.1000000 0.7500000 3.750000 3
## [20600] {dataset,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20601] {network,
## neural,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20602] {network,
## dataset,
## neural,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20603] {network,
## dataset,
## perform,
## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [20604] {network,
## dataset,
## neural,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20605] {approach,
## show,
## algorithm,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20606] {approach,
## network,
## algorithm,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [20607] {network,
## show,
## algorithm,
## process} => {approach} 0.1000000 1.0000000 2.500000 3
## [20608] {approach,
## network,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20609] {approach,
## network,
## show,
## algorithm} => {process} 0.1000000 1.0000000 5.000000 3
## [20610] {algorithm,
## dataset,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [20611] {network,
## algorithm,
## perform,
## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20612] {network,
## algorithm,
## dataset,
## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [20613] {network,
## dataset,
## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20614] {network,
## algorithm,
## dataset,
## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [20615] {model,
## show,
## algorithm,
## process} => {featur} 0.1000000 1.0000000 1.875000 3
## [20616] {featur,
## show,
## algorithm,
## process} => {model} 0.1000000 1.0000000 1.875000 3
## [20617] {featur,
## model,
## algorithm,
## process} => {show} 0.1000000 1.0000000 1.875000 3
## [20618] {featur,
## model,
## show,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [20619] {featur,
## model,
## show,
## algorithm} => {process} 0.1000000 0.7500000 3.750000 3
## [20620] {approach,
## perform,
## demonstr,
## problem} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20621] {approach,
## dataset,
## demonstr,
## problem} => {perform} 0.1000000 1.0000000 2.142857 3
## [20622] {dataset,
## perform,
## demonstr,
## problem} => {approach} 0.1000000 1.0000000 2.500000 3
## [20623] {approach,
## dataset,
## perform,
## demonstr} => {problem} 0.1000000 1.0000000 3.333333 3
## [20624] {approach,
## dataset,
## perform,
## problem} => {demonstr} 0.1000000 0.7500000 3.214286 3
## [20625] {model,
## dataset,
## demonstr,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20626] {featur,
## dataset,
## demonstr,
## learn} => {model} 0.1000000 1.0000000 1.875000 3
## [20627] {featur,
## model,
## dataset,
## demonstr} => {learn} 0.1000000 1.0000000 2.307692 3
## [20628] {featur,
## model,
## demonstr,
## learn} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20629] {data,
## reduc,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [20630] {network,
## reduc,
## task,
## achiev} => {data} 0.1000000 1.0000000 2.307692 3
## [20631] {data,
## network,
## reduc,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [20632] {data,
## network,
## reduc,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20633] {data,
## network,
## task,
## achiev} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20634] {data,
## paper,
## reduc,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20635] {network,
## paper,
## reduc,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20636] {data,
## network,
## paper,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [20637] {data,
## network,
## reduc,
## task} => {paper} 0.1000000 0.7500000 2.250000 3
## [20638] {data,
## network,
## paper,
## task} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20639] {approach,
## method,
## reduc,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [20640] {approach,
## method,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [20641] {method,
## network,
## reduc,
## show} => {approach} 0.1000000 1.0000000 2.500000 3
## [20642] {approach,
## network,
## reduc,
## show} => {method} 0.1000000 1.0000000 2.727273 3
## [20643] {approach,
## method,
## network,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20644] {data,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20645] {data,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20646] {reduc,
## represent,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20647] {data,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20648] {data,
## reduc,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20649] {data,
## featur,
## reduc,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20650] {featur,
## reduc,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20651] {data,
## featur,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [20652] {data,
## reduc,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20653] {data,
## network,
## reduc,
## task} => {represent} 0.1000000 0.7500000 1.500000 3
## [20654] {network,
## reduc,
## represent,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20655] {data,
## network,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [20656] {data,
## network,
## represent,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20657] {data,
## reduc,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20658] {data,
## featur,
## reduc,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20659] {featur,
## reduc,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20660] {data,
## featur,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20661] {data,
## reduc,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20662] {data,
## network,
## reduc,
## task} => {show} 0.1000000 0.7500000 1.406250 3
## [20663] {network,
## reduc,
## show,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20664] {data,
## network,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20665] {data,
## network,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20666] {data,
## featur,
## reduc,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20667] {data,
## network,
## reduc,
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## [20668] {featur,
## network,
## reduc,
## task} => {data} 0.1000000 1.0000000 2.307692 3
## [20669] {data,
## featur,
## network,
## reduc} => {task} 0.1000000 1.0000000 2.727273 3
## [20670] {data,
## featur,
## network,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20671] {reduc,
## represent,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20672] {featur,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20673] {featur,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20674] {featur,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20675] {featur,
## represent,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20676] {reduc,
## represent,
## show,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20677] {network,
## reduc,
## represent,
## task} => {show} 0.1000000 1.0000000 1.875000 3
## [20678] {network,
## reduc,
## show,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20679] {network,
## reduc,
## represent,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20680] {network,
## represent,
## show,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20681] {featur,
## reduc,
## represent,
## task} => {network} 0.1000000 1.0000000 1.578947 3
## [20682] {network,
## reduc,
## represent,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20683] {featur,
## network,
## reduc,
## task} => {represent} 0.1000000 1.0000000 2.000000 3
## [20684] {featur,
## network,
## reduc,
## represent} => {task} 0.1000000 1.0000000 2.727273 3
## [20685] {featur,
## network,
## represent,
## task} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20686] {featur,
## reduc,
## show,
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## [20687] {network,
## reduc,
## show,
## task} => {featur} 0.1000000 1.0000000 1.875000 3
## [20688] {featur,
## network,
## reduc,
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## [20689] {featur,
## network,
## reduc,
## show} => {task} 0.1000000 1.0000000 2.727273 3
## [20690] {featur,
## network,
## show,
## task} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20691] {data,
## reduc,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20692] {data,
## featur,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [20693] {data,
## featur,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [20694] {featur,
## reduc,
## represent,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [20695] {data,
## featur,
## represent,
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## [20696] {data,
## reduc,
## represent,
## show} => {network} 0.1000000 1.0000000 1.578947 3
## [20697] {data,
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## reduc,
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## [20698] {data,
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## reduc,
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## [20699] {network,
## reduc,
## represent,
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## [20700] {data,
## network,
## represent,
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## [20701] {data,
## featur,
## reduc,
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## [20702] {data,
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## reduc,
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## [20703] {data,
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## [20704] {featur,
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## reduc,
## represent} => {data} 0.1000000 1.0000000 2.307692 3
## [20705] {data,
## featur,
## network,
## represent} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20706] {data,
## featur,
## reduc,
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## [20707] {data,
## network,
## reduc,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20708] {data,
## featur,
## network,
## reduc} => {show} 0.1000000 1.0000000 1.875000 3
## [20709] {featur,
## network,
## reduc,
## show} => {data} 0.1000000 1.0000000 2.307692 3
## [20710] {data,
## featur,
## network,
## show} => {reduc} 0.1000000 1.0000000 4.285714 3
## [20711] {featur,
## reduc,
## represent,
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## [20712] {network,
## reduc,
## represent,
## show} => {featur} 0.1000000 1.0000000 1.875000 3
## [20713] {featur,
## network,
## reduc,
## represent} => {show} 0.1000000 1.0000000 1.875000 3
## [20714] {featur,
## network,
## reduc,
## show} => {represent} 0.1000000 1.0000000 2.000000 3
## [20715] {featur,
## network,
## represent,
## show} => {reduc} 0.1000000 0.7500000 3.214286 3
## [20716] {paper,
## train,
## achiev,
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## [20717] {paper,
## represent,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20718] {paper,
## represent,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [20719] {represent,
## train,
## achiev,
## learn} => {paper} 0.1000000 1.0000000 3.000000 3
## [20720] {paper,
## represent,
## train,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20721] {paper,
## train,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20722] {featur,
## paper,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20723] {featur,
## paper,
## achiev,
## learn} => {train} 0.1000000 1.0000000 2.500000 3
## [20724] {featur,
## train,
## achiev,
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## [20725] {featur,
## paper,
## train,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20726] {paper,
## represent,
## train,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [20727] {featur,
## paper,
## train,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [20728] {featur,
## paper,
## represent,
## achiev} => {train} 0.1000000 1.0000000 2.500000 3
## [20729] {featur,
## represent,
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## achiev} => {paper} 0.1000000 1.0000000 3.000000 3
## [20730] {featur,
## paper,
## represent,
## train} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20731] {paper,
## represent,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20732] {featur,
## paper,
## achiev,
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## [20733] {featur,
## paper,
## represent,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20734] {featur,
## represent,
## achiev,
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## [20735] {featur,
## paper,
## represent,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20736] {achiev,
## dataset,
## improv,
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## [20737] {achiev,
## improv,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20738] {achiev,
## dataset,
## improv,
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## [20739] {achiev,
## dataset,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [20740] {dataset,
## improv,
## neural,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20741] {achiev,
## dataset,
## improv,
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## [20742] {model,
## achiev,
## improv,
## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20743] {model,
## achiev,
## dataset,
## improv} => {neural} 0.1000000 1.0000000 3.000000 3
## [20744] {model,
## achiev,
## dataset,
## neural} => {improv} 0.1000000 1.0000000 3.333333 3
## [20745] {model,
## dataset,
## improv,
## neural} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20746] {achiev,
## improv,
## neural,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20747] {model,
## achiev,
## improv,
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## [20748] {model,
## achiev,
## improv,
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## [20749] {model,
## achiev,
## neural,
## propos} => {improv} 0.1000000 1.0000000 3.333333 3
## [20750] {model,
## improv,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20751] {achiev,
## dataset,
## improv,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20752] {model,
## achiev,
## dataset,
## improv} => {propos} 0.1000000 1.0000000 2.000000 3
## [20753] {model,
## achiev,
## improv,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20754] {model,
## achiev,
## dataset,
## propos} => {improv} 0.1000000 0.7500000 2.500000 3
## [20755] {model,
## dataset,
## improv,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20756] {approach,
## achiev,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [20757] {achiev,
## neural,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [20758] {approach,
## achiev,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [20759] {approach,
## achiev,
## neural,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [20760] {approach,
## neural,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20761] {approach,
## achiev,
## neural,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [20762] {network,
## achiev,
## neural,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [20763] {approach,
## network,
## achiev,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [20764] {approach,
## network,
## achiev,
## neural} => {result} 0.1000000 1.0000000 3.000000 3
## [20765] {approach,
## network,
## neural,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20766] {achiev,
## neural,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [20767] {network,
## achiev,
## neural,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [20768] {network,
## achiev,
## propos,
## result} => {neural} 0.1000000 1.0000000 3.000000 3
## [20769] {network,
## achiev,
## neural,
## propos} => {result} 0.1000000 1.0000000 3.000000 3
## [20770] {network,
## neural,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20771] {approach,
## achiev,
## propos,
## result} => {network} 0.1000000 1.0000000 1.578947 3
## [20772] {approach,
## network,
## achiev,
## result} => {propos} 0.1000000 1.0000000 2.000000 3
## [20773] {network,
## achiev,
## propos,
## result} => {approach} 0.1000000 1.0000000 2.500000 3
## [20774] {approach,
## network,
## achiev,
## propos} => {result} 0.1000000 0.7500000 2.250000 3
## [20775] {approach,
## network,
## propos,
## result} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20776] {approach,
## achiev,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20777] {approach,
## network,
## achiev,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [20778] {network,
## achiev,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20779] {approach,
## network,
## achiev,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [20780] {achiev,
## dataset,
## neural,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20781] {model,
## achiev,
## dataset,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [20782] {model,
## achiev,
## neural,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20783] {model,
## achiev,
## dataset,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [20784] {model,
## dataset,
## neural,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20785] {approach,
## method,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [20786] {approach,
## featur,
## method,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [20787] {featur,
## method,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20788] {approach,
## featur,
## achiev,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [20789] {approach,
## featur,
## method,
## propos} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20790] {approach,
## method,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20791] {approach,
## method,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [20792] {method,
## network,
## achiev,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20793] {approach,
## network,
## achiev,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [20794] {approach,
## method,
## network,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20795] {approach,
## featur,
## method,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [20796] {approach,
## method,
## network,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [20797] {featur,
## method,
## network,
## achiev} => {approach} 0.1000000 1.0000000 2.500000 3
## [20798] {approach,
## featur,
## network,
## achiev} => {method} 0.1000000 1.0000000 2.727273 3
## [20799] {approach,
## featur,
## method,
## network} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20800] {featur,
## method,
## achiev,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20801] {method,
## network,
## achiev,
## propos} => {featur} 0.1000000 1.0000000 1.875000 3
## [20802] {featur,
## method,
## network,
## achiev} => {propos} 0.1000000 1.0000000 2.000000 3
## [20803] {featur,
## network,
## achiev,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [20804] {featur,
## method,
## network,
## propos} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20805] {represent,
## task,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20806] {featur,
## task,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20807] {featur,
## represent,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20808] {featur,
## represent,
## achiev,
## learn} => {task} 0.1000000 0.7500000 2.045455 3
## [20809] {represent,
## task,
## achiev,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [20810] {network,
## task,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20811] {network,
## represent,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20812] {network,
## represent,
## achiev,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [20813] {network,
## represent,
## task,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20814] {featur,
## task,
## achiev,
## learn} => {network} 0.1000000 1.0000000 1.578947 3
## [20815] {network,
## task,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20816] {featur,
## network,
## task,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20817] {featur,
## network,
## achiev,
## learn} => {task} 0.1000000 1.0000000 2.727273 3
## [20818] {featur,
## network,
## task,
## learn} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20819] {featur,
## represent,
## task,
## achiev} => {network} 0.1000000 1.0000000 1.578947 3
## [20820] {network,
## represent,
## task,
## achiev} => {featur} 0.1000000 1.0000000 1.875000 3
## [20821] {featur,
## network,
## task,
## achiev} => {represent} 0.1000000 1.0000000 2.000000 3
## [20822] {featur,
## network,
## represent,
## achiev} => {task} 0.1000000 1.0000000 2.727273 3
## [20823] {featur,
## network,
## represent,
## task} => {achiev} 0.1000000 0.7500000 3.214286 3
## [20824] {represent,
## train,
## achiev,
## learn} => {featur} 0.1000000 1.0000000 1.875000 3
## [20825] {featur,
## train,
## achiev,
## learn} => {represent} 0.1000000 1.0000000 2.000000 3
## [20826] {featur,
## represent,
## train,
## achiev} => {learn} 0.1000000 1.0000000 2.307692 3
## [20827] {featur,
## represent,
## achiev,
## learn} => {train} 0.1000000 0.7500000 1.875000 3
## [20828] {featur,
## represent,
## train,
## learn} => {achiev} 0.1000000 1.0000000 4.285714 3
## [20829] {approach,
## achiev,
## dataset,
## propos} => {model} 0.1000000 1.0000000 1.875000 3
## [20830] {approach,
## model,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [20831] {approach,
## model,
## achiev,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [20832] {model,
## achiev,
## dataset,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [20833] {approach,
## achiev,
## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [20834] {approach,
## network,
## achiev,
## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [20835] {approach,
## network,
## achiev,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [20836] {network,
## achiev,
## dataset,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [20837] {approach,
## model,
## achiev,
## dataset} => {network} 0.1000000 1.0000000 1.578947 3
## [20838] {approach,
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## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21219] {network,
## architectur,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21220] {approach,
## network,
## architectur,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21221] {approach,
## network,
## experi,
## neural} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21222] {approach,
## network,
## architectur,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21223] {architectur,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21224] {network,
## architectur,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21225] {network,
## architectur,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21226] {network,
## experi,
## neural,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21227] {network,
## architectur,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21228] {approach,
## method,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21229] {method,
## architectur,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21230] {approach,
## architectur,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21231] {approach,
## method,
## experi,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21232] {approach,
## method,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21233] {approach,
## method,
## architectur,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [21234] {method,
## network,
## architectur,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [21235] {approach,
## network,
## architectur,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [21236] {approach,
## method,
## network,
## experi} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21237] {approach,
## method,
## network,
## architectur} => {experi} 0.1000000 1.0000000 3.750000 3
## [21238] {method,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21239] {method,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21240] {network,
## architectur,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21241] {method,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21242] {method,
## network,
## architectur,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21243] {approach,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21244] {approach,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21245] {network,
## architectur,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21246] {approach,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21247] {approach,
## network,
## architectur,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21248] {architectur,
## dataset,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21249] {network,
## architectur,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21250] {network,
## architectur,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21251] {network,
## dataset,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21252] {network,
## architectur,
## dataset,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21253] {featur,
## architectur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21254] {network,
## architectur,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21255] {featur,
## network,
## architectur,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21256] {featur,
## network,
## experi,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21257] {featur,
## network,
## architectur,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21258] {classif,
## method,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21259] {approach,
## classif,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21260] {approach,
## classif,
## method,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21261] {approach,
## method,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21262] {approach,
## classif,
## method,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21263] {classif,
## method,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21264] {classif,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21265] {classif,
## method,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21266] {method,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21267] {classif,
## method,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21268] {classif,
## method,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21269] {classif,
## network,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21270] {classif,
## method,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21271] {method,
## network,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21272] {classif,
## method,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21273] {approach,
## classif,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21274] {classif,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21275] {approach,
## classif,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21276] {approach,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21277] {approach,
## classif,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21278] {approach,
## classif,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21279] {classif,
## network,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21280] {approach,
## classif,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21281] {approach,
## network,
## experi,
## neural} => {classif} 0.1000000 1.0000000 3.750000 3
## [21282] {approach,
## classif,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21283] {classif,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21284] {classif,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21285] {classif,
## network,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21286] {network,
## experi,
## neural,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21287] {classif,
## network,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21288] {approach,
## classif,
## method,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21289] {classif,
## method,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21290] {approach,
## classif,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21291] {approach,
## method,
## experi,
## propos} => {classif} 0.1000000 0.7500000 2.812500 3
## [21292] {approach,
## classif,
## method,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21293] {approach,
## classif,
## method,
## experi} => {network} 0.1000000 1.0000000 1.578947 3
## [21294] {classif,
## method,
## network,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [21295] {approach,
## classif,
## network,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [21296] {approach,
## method,
## network,
## experi} => {classif} 0.1000000 1.0000000 3.750000 3
## [21297] {approach,
## classif,
## method,
## network} => {experi} 0.1000000 0.7500000 2.812500 3
## [21298] {classif,
## method,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21299] {classif,
## method,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21300] {classif,
## network,
## experi,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21301] {method,
## network,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21302] {classif,
## method,
## network,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21303] {approach,
## classif,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21304] {approach,
## classif,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21305] {classif,
## network,
## experi,
## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21306] {approach,
## network,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21307] {approach,
## classif,
## network,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21308] {classif,
## dataset,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21309] {classif,
## network,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21310] {classif,
## network,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21311] {network,
## dataset,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21312] {classif,
## network,
## dataset,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21313] {classif,
## featur,
## experi,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21314] {classif,
## network,
## experi,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21315] {classif,
## featur,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21316] {featur,
## network,
## experi,
## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [21317] {classif,
## featur,
## network,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21318] {approach,
## method,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21319] {method,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21320] {approach,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21321] {approach,
## method,
## experi,
## propos} => {neural} 0.1000000 0.7500000 2.250000 3
## [21322] {approach,
## method,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21323] {approach,
## method,
## experi,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21324] {method,
## network,
## experi,
## neural} => {approach} 0.1000000 1.0000000 2.500000 3
## [21325] {approach,
## network,
## experi,
## neural} => {method} 0.1000000 1.0000000 2.727273 3
## [21326] {approach,
## method,
## network,
## experi} => {neural} 0.1000000 1.0000000 3.000000 3
## [21327] {approach,
## method,
## network,
## neural} => {experi} 0.1000000 1.0000000 3.750000 3
## [21328] {method,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21329] {method,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21330] {network,
## experi,
## neural,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21331] {method,
## network,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21332] {method,
## network,
## neural,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21333] {approach,
## experi,
## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21334] {approach,
## network,
## experi,
## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [21335] {network,
## experi,
## neural,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21336] {approach,
## network,
## experi,
## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [21337] {method,
## algorithm,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21338] {method,
## show,
## algorithm,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21339] {method,
## show,
## experi,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [21340] {show,
## algorithm,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21341] {method,
## show,
## algorithm,
## perform} => {experi} 0.1000000 1.0000000 3.750000 3
## [21342] {approach,
## method,
## experi,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21343] {approach,
## method,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21344] {method,
## dataset,
## experi,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [21345] {approach,
## dataset,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21346] {approach,
## method,
## dataset,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21347] {approach,
## method,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21348] {approach,
## method,
## show,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21349] {method,
## show,
## experi,
## perform} => {approach} 0.1000000 0.7500000 1.875000 3
## [21350] {approach,
## show,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21351] {approach,
## method,
## show,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21352] {approach,
## method,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21353] {approach,
## method,
## experi,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [21354] {method,
## experi,
## perform,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21355] {approach,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21356] {approach,
## method,
## perform,
## propos} => {experi} 0.1000000 1.0000000 3.750000 3
## [21357] {approach,
## method,
## dataset,
## experi} => {show} 0.1000000 1.0000000 1.875000 3
## [21358] {approach,
## method,
## show,
## experi} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21359] {method,
## show,
## dataset,
## experi} => {approach} 0.1000000 1.0000000 2.500000 3
## [21360] {approach,
## show,
## dataset,
## experi} => {method} 0.1000000 1.0000000 2.727273 3
## [21361] {approach,
## method,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21362] {approach,
## method,
## experi,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21363] {method,
## dataset,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21364] {approach,
## dataset,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21365] {approach,
## method,
## dataset,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21366] {approach,
## method,
## show,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21367] {approach,
## method,
## experi,
## propos} => {show} 0.1000000 0.7500000 1.406250 3
## [21368] {method,
## show,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21369] {approach,
## show,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21370] {approach,
## method,
## show,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21371] {approach,
## method,
## experi,
## propos} => {network} 0.1000000 0.7500000 1.184211 3
## [21372] {approach,
## method,
## network,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21373] {method,
## network,
## experi,
## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21374] {approach,
## network,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21375] {approach,
## method,
## network,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21376] {method,
## dataset,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21377] {method,
## show,
## experi,
## perform} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21378] {method,
## show,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21379] {show,
## dataset,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21380] {method,
## show,
## dataset,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21381] {method,
## dataset,
## experi,
## perform} => {propos} 0.1000000 1.0000000 2.000000 3
## [21382] {method,
## experi,
## perform,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21383] {method,
## dataset,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21384] {dataset,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21385] {method,
## dataset,
## perform,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21386] {method,
## show,
## experi,
## perform} => {propos} 0.1000000 0.7500000 1.500000 3
## [21387] {method,
## experi,
## perform,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21388] {method,
## show,
## experi,
## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21389] {show,
## experi,
## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21390] {method,
## show,
## perform,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21391] {method,
## show,
## experi,
## perform} => {model} 0.1000000 0.7500000 1.406250 3
## [21392] {method,
## model,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21393] {method,
## model,
## show,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21394] {model,
## show,
## experi,
## perform} => {method} 0.1000000 1.0000000 2.727273 3
## [21395] {method,
## model,
## show,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
## [21396] {method,
## show,
## dataset,
## experi} => {propos} 0.1000000 1.0000000 2.000000 3
## [21397] {method,
## dataset,
## experi,
## propos} => {show} 0.1000000 1.0000000 1.875000 3
## [21398] {method,
## show,
## experi,
## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21399] {show,
## dataset,
## experi,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21400] {method,
## show,
## dataset,
## propos} => {experi} 0.1000000 0.7500000 2.812500 3
## [21401] {approach,
## dataset,
## experi,
## perform} => {show} 0.1000000 1.0000000 1.875000 3
## [21402] {approach,
## show,
## experi,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21403] {approach,
## show,
## dataset,
## experi} => {perform} 0.1000000 1.0000000 2.142857 3
## [21404] {show,
## dataset,
## experi,
## perform} => {approach} 0.1000000 1.0000000 2.500000 3
## [21405] {approach,
## show,
## dataset,
## perform} => {experi} 0.1000000 0.7500000 2.812500 3
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## [21410] {approach,
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## [21411] {approach,
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## [21412] {approach,
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## [21413] {approach,
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## [21414] {show,
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## [21415] {approach,
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## [21416] {approach,
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## [21419] {show,
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## [21429] {classif,
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## [21637] {classif,
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## [21645] {algorithm,
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## [21657] {network,
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## [21658] {architectur,
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## [21661] {architectur,
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## [21667] {network,
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## [21675] {architectur,
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## [21676] {algorithm,
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## [21677] {algorithm,
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## [21678] {algorithm,
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## [21679] {network,
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## [21683] {algorithm,
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## [21684] {network,
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## [21685] {network,
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## [21686] {network,
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## [21687] {network,
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## [21688] {architectur,
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## [21689] {network,
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## [21690] {network,
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## [21691] {network,
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## [21692] {network,
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## [21693] {architectur,
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## [21695] {network,
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## [21696] {network,
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## [21697] {network,
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## [21698] {featur,
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## [21705] {approach,
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## [21706] {approach,
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## [21707] {approach,
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## [21708] {approach,
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## [21709] {method,
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## [21710] {approach,
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## [21711] {approach,
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## [21712] {approach,
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## [21713] {method,
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## [21714] {method,
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## [21715] {network,
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## [21716] {method,
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## [21718] {algorithm,
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## [21719] {algorithm,
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## [21720] {architectur,
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## neural,
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## [21721] {algorithm,
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## dataset,
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## [21722] {algorithm,
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## neural,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21723] {algorithm,
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## neural,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21724] {network,
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## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [21725] {network,
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## [21726] {network,
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## [21727] {network,
## algorithm,
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## [21728] {algorithm,
## architectur,
## dataset,
## neural} => {network} 0.1000000 1.0000000 1.578947 3
## [21729] {network,
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## neural} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21730] {network,
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## dataset,
## neural} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [21731] {network,
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## [21732] {network,
## algorithm,
## dataset,
## neural} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21733] {approach,
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## neural,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21734] {approach,
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## [21735] {network,
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## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [21736] {approach,
## network,
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## [21737] {architectur,
## dataset,
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## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21738] {network,
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## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21739] {network,
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## dataset,
## neural} => {perform} 0.1000000 1.0000000 2.142857 3
## [21740] {network,
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## dataset,
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## [21741] {network,
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## [21742] {approach,
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## [21743] {approach,
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## [21744] {method,
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## propos} => {approach} 0.1000000 0.7500000 1.875000 3
## [21745] {approach,
## network,
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## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21746] {approach,
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## [21747] {method,
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## [21748] {method,
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## propos} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21749] {method,
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## propos} => {perform} 0.1000000 1.0000000 2.142857 3
## [21750] {architectur,
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## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21751] {method,
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## perform,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21752] {method,
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## dataset,
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## [21753] {method,
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## [21754] {method,
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## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [21755] {network,
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## dataset,
## perform} => {method} 0.1000000 0.7500000 2.045455 3
## [21756] {method,
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## [21757] {method,
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## perform,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21758] {method,
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## [21759] {method,
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## architectur,
## propos} => {perform} 0.1000000 0.7500000 1.607143 3
## [21760] {network,
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## perform,
## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [21761] {method,
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## perform,
## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21762] {method,
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## dataset,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21763] {method,
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## dataset} => {propos} 0.1000000 1.0000000 2.000000 3
## [21764] {method,
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## architectur,
## propos} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21765] {network,
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## dataset,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21766] {method,
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## dataset,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21767] {featur,
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## architectur,
## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [21768] {method,
## network,
## architectur,
## propos} => {featur} 0.1000000 0.7500000 1.406250 3
## [21769] {featur,
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## network,
## architectur} => {propos} 0.1000000 1.0000000 2.000000 3
## [21770] {featur,
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## architectur,
## propos} => {method} 0.1000000 0.7500000 2.045455 3
## [21771] {featur,
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## network,
## propos} => {architectur} 0.1000000 0.7500000 2.812500 3
## [21772] {algorithm,
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## dataset,
## perform} => {network} 0.1000000 1.0000000 1.578947 3
## [21773] {network,
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## architectur,
## perform} => {dataset} 0.1000000 1.0000000 2.307692 3
## [21774] {network,
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## architectur,
## dataset} => {perform} 0.1000000 1.0000000 2.142857 3
## [21775] {network,
## architectur,
## dataset,
## perform} => {algorithm} 0.1000000 0.7500000 1.875000 3
## [21776] {network,
## algorithm,
## dataset,
## perform} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21777] {architectur,
## dataset,
## perform,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [21778] {network,
## architectur,
## perform,
## work} => {dataset} 0.1000000 0.7500000 1.730769 3
## [21779] {network,
## architectur,
## dataset,
## work} => {perform} 0.1000000 0.7500000 1.607143 3
## [21780] {network,
## architectur,
## dataset,
## perform} => {work} 0.1000000 0.7500000 1.875000 3
## [21781] {network,
## dataset,
## perform,
## work} => {architectur} 0.1000000 1.0000000 3.750000 3
## [21782] {architectur,
## dataset,
## propos,
## work} => {network} 0.1000000 1.0000000 1.578947 3
## [21783] {network,
## architectur,
## dataset,
## work} => {propos} 0.1000000 0.7500000 1.500000 3
## [21784] {network,
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## [24949] {boltzmann,
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## [24961] {boltzmann,
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## [24979] {boltzmann,
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## [24990] {boltzmann,
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## [24991] {boltzmann,
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## [28872] {data,
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## [28876] {boltzmann,
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## [28878] {boltzmann,
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## [28879] {boltzmann,
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## [28880] {boltzmann,
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## [28881] {boltzmann,
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## [28883] {data,
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## [28884] {data,
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## [28885] {data,
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## [28886] {data,
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## [28887] {featur,
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## [28890] {data,
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## [28893] {approach,
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## [28894] {approach,
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## [28895] {approach,
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## [28897] {approach,
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## [28898] {approach,
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## [28899] {approach,
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## [28900] {approach,
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## [28901] {approach,
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## [28902] {approach,
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## [28903] {approach,
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## [28904] {data,
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## [28905] {approach,
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## [28906] {approach,
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## [28907] {approach,
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## [28908] {approach,
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## [28909] {approach,
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## [28910] {approach,
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## [28911] {approach,
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## [28912] {data,
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## [28913] {approach,
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## semant} => {task} 0.1000000 1.0000000 2.727273 3
## [28914] {approach,
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## [28915] {approach,
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## [28916] {approach,
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## [28917] {approach,
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## [28918] {approach,
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## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28919] {approach,
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## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28920] {data,
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## [28921] {approach,
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## [28922] {approach,
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## [28923] {approach,
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## [28924] {approach,
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## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28925] {approach,
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## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28926] {approach,
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## semant} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28927] {approach,
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## [28928] {represent,
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## [28929] {approach,
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## [28930] {approach,
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## [28931] {approach,
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## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28932] {approach,
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## [28933] {approach,
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## semant} => {learn} 0.1000000 1.0000000 2.307692 3
## [28934] {approach,
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## [28935] {approach,
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## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28936] {data,
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## [28937] {approach,
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## [28938] {approach,
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## [28939] {data,
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## semant} => {propos} 0.1000000 1.0000000 2.000000 3
## [28940] {data,
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## semant} => {represent} 0.1000000 1.0000000 2.000000 3
## [28941] {data,
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## [28942] {data,
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## [28943] {represent,
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## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28944] {data,
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## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28945] {data,
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## [28946] {data,
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## [28947] {approach,
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## [28948] {approach,
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## [28949] {approach,
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## [28950] {approach,
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## [28951] {approach,
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## semant} => {data} 0.1000000 1.0000000 2.307692 3
## [28952] {approach,
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## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28953] {data,
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## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28954] {approach,
## data,
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## learn} => {semant} 0.1000000 1.0000000 6.000000 3
## [28955] {algorithm,
## architectur,
## dataset,
## improv,
## neural,
## perform,
## process} => {network} 0.1000000 1.0000000 1.578947 3
## [28956] {network,
## algorithm,
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## process} => {dataset} 0.1000000 1.0000000 2.307692 3
## [28957] {network,
## algorithm,
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## process} => {perform} 0.1000000 1.0000000 2.142857 3
## [28958] {network,
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## perform,
## process} => {algorithm} 0.1000000 1.0000000 2.500000 3
## [28959] {network,
## algorithm,
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## process} => {neural} 0.1000000 1.0000000 3.000000 3
## [28960] {network,
## algorithm,
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## neural,
## perform,
## process} => {improv} 0.1000000 1.0000000 3.333333 3
## [28961] {network,
## algorithm,
## dataset,
## improv,
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## perform,
## process} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28962] {network,
## algorithm,
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## perform} => {process} 0.1000000 1.0000000 5.000000 3
## [28963] {approach,
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## propos} => {network} 0.1000000 1.0000000 1.578947 3
## [28964] {approach,
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## neural} => {propos} 0.1000000 1.0000000 2.000000 3
## [28965] {classif,
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## propos} => {approach} 0.1000000 1.0000000 2.500000 3
## [28966] {approach,
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## propos} => {method} 0.1000000 1.0000000 2.727273 3
## [28967] {approach,
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## propos} => {neural} 0.1000000 1.0000000 3.000000 3
## [28968] {approach,
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## propos} => {classif} 0.1000000 1.0000000 3.750000 3
## [28969] {approach,
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## propos} => {architectur} 0.1000000 1.0000000 3.750000 3
## [28970] {approach,
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## [28971] {approach,
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## [28972] {approach,
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## [28973] {approach,
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## [28974] {approach,
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## [28975] {approach,
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## [28976] {approach,
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## [28977] {data,
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## [28978] {approach,
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## [28979] {data,
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## [28980] {data,
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## [28981] {data,
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## [28982] {data,
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## [28983] {data,
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## [28984] {featur,
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## [28985] {data,
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## [28986] {data,
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## [28987] {approach,
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## [28988] {approach,
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## [28989] {approach,
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## [28990] {approach,
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## [28991] {approach,
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## [28992] {approach,
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## semant} => {work} 0.1000000 1.0000000 2.500000 3
## [28993] {data,
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## semant} => {approach} 0.1000000 1.0000000 2.500000 3
## [28994] {approach,
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## [28995] {approach,
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## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 10
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[1891 item(s), 102 transaction(s)] done [0.00s].
## sorting and recoding items ... [121 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [781 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support confidence
## [1] {experiment} => {result} 0.1078431 0.8461538
## [2] {practic} => {network} 0.1078431 0.9166667
## [3] {robust} => {network} 0.1176471 1.0000000
## [4] {optim} => {network} 0.1078431 0.9166667
## [5] {find} => {perform} 0.1176471 0.9230769
## [6] {util} => {network} 0.1078431 0.9166667
## [7] {order} => {network} 0.1176471 0.9230769
## [8] {cnn} => {convolut} 0.1078431 0.8461538
## [9] {cnn} => {neural} 0.1176471 0.9230769
## [10] {cnn} => {network} 0.1176471 0.9230769
## [11] {general} => {network} 0.1078431 0.7333333
## [12] {design} => {network} 0.1274510 0.8666667
## [13] {specif} => {paper} 0.1078431 0.7333333
## [14] {specif} => {network} 0.1078431 0.7333333
## [15] {natur} => {neural} 0.1078431 0.7857143
## [16] {natur} => {network} 0.1176471 0.8571429
## [17] {potenti} => {perform} 0.1078431 0.7333333
## [18] {potenti} => {network} 0.1078431 0.7333333
## [19] {exist} => {method} 0.1078431 0.7857143
## [20] {paramet} => {train} 0.1176471 0.8571429
## [21] {paramet} => {perform} 0.1078431 0.7857143
## [22] {addit} => {neural} 0.1176471 0.8000000
## [23] {addit} => {network} 0.1274510 0.8666667
## [24] {introduc} => {show} 0.1176471 0.7058824
## [25] {introduc} => {train} 0.1176471 0.7058824
## [26] {introduc} => {neural} 0.1274510 0.7647059
## [27] {introduc} => {network} 0.1372549 0.8235294
## [28] {generat} => {model} 0.1176471 0.8000000
## [29] {generat} => {neural} 0.1078431 0.7333333
## [30] {experi} => {paper} 0.1176471 0.7058824
## [31] {experi} => {perform} 0.1176471 0.7058824
## [32] {experi} => {network} 0.1372549 0.8235294
## [33] {represent} => {model} 0.1274510 0.7647059
## [34] {represent} => {neural} 0.1176471 0.7058824
## [35] {input} => {neural} 0.1176471 0.7500000
## [36] {input} => {network} 0.1470588 0.9375000
## [37] {extract} => {featur} 0.1372549 0.7777778
## [38] {extract} => {neural} 0.1470588 0.8333333
## [39] {extract} => {network} 0.1568627 0.8888889
## [40] {signific} => {train} 0.1176471 0.7058824
## [41] {signific} => {perform} 0.1176471 0.7058824
## [42] {visual} => {base} 0.1176471 0.7058824
## [43] {visual} => {model} 0.1372549 0.8235294
## [44] {visual} => {neural} 0.1274510 0.7647059
## [45] {visual} => {network} 0.1666667 1.0000000
## [46] {high} => {network} 0.1470588 0.8333333
## [47] {provid} => {model} 0.1274510 0.7647059
## [48] {provid} => {network} 0.1176471 0.7058824
## [49] {challeng} => {network} 0.1470588 0.7894737
## [50] {layer} => {show} 0.1176471 0.7058824
## [51] {layer} => {neural} 0.1470588 0.8823529
## [52] {layer} => {network} 0.1372549 0.8235294
## [53] {classifi} => {classif} 0.1372549 0.7000000
## [54] {classifi} => {neural} 0.1568627 0.8000000
## [55] {classifi} => {network} 0.1862745 0.9500000
## [56] {time} => {network} 0.1470588 0.7894737
## [57] {predict} => {network} 0.1568627 0.7619048
## [58] {recent} => {network} 0.1470588 0.7894737
## [59] {combin} => {neural} 0.1666667 0.8095238
## [60] {combin} => {network} 0.1862745 0.9047619
## [61] {problem} => {paper} 0.1764706 0.8571429
## [62] {problem} => {neural} 0.1470588 0.7142857
## [63] {problem} => {network} 0.1666667 0.8095238
## [64] {recognit} => {model} 0.1372549 0.7368421
## [65] {recognit} => {network} 0.1568627 0.8421053
## [66] {appli} => {paper} 0.1470588 0.7142857
## [67] {learn} => {model} 0.1372549 0.7000000
## [68] {learn} => {network} 0.1568627 0.8000000
## [69] {develop} => {comput} 0.1372549 0.7000000
## [70] {develop} => {network} 0.1568627 0.8000000
## [71] {system} => {network} 0.1666667 0.7727273
## [72] {set} => {network} 0.1862745 0.8260870
## [73] {effici} => {network} 0.1666667 0.7391304
## [74] {object} => {network} 0.1666667 0.8095238
## [75] {larg} => {network} 0.2156863 0.9565217
## [76] {compar} => {perform} 0.1666667 0.7083333
## [77] {framework} => {network} 0.1960784 0.7692308
## [78] {classif} => {network} 0.2058824 0.7500000
## [79] {inform} => {network} 0.1862745 0.7307692
## [80] {machin} => {network} 0.2058824 0.7777778
## [81] {work} => {network} 0.1960784 0.7407407
## [82] {present} => {neural} 0.2058824 0.7241379
## [83] {present} => {network} 0.2352941 0.8275862
## [84] {accuraci} => {network} 0.2254902 0.7931034
## [85] {dataset} => {train} 0.2254902 0.7419355
## [86] {dataset} => {network} 0.2254902 0.7419355
## [87] {architectur} => {network} 0.2450980 0.8064516
## [88] {improv} => {network} 0.2254902 0.7187500
## [89] {imag} => {network} 0.2647059 0.8709677
## [90] {achiev} => {network} 0.2745098 0.8484848
## [91] {task} => {network} 0.2352941 0.7272727
## [92] {base} => {network} 0.2745098 0.8484848
## [93] {featur} => {network} 0.2745098 0.8235294
## [94] {approach} => {network} 0.2647059 0.7500000
## [95] {algorithm} => {network} 0.2549020 0.7027027
## [96] {comput} => {network} 0.2941176 0.8108108
## [97] {propos} => {network} 0.2843137 0.7435897
## [98] {data} => {network} 0.2843137 0.7435897
## [99] {convolut} => {network} 0.3725490 0.9743590
## [100] {show} => {network} 0.3725490 0.7916667
## [101] {train} => {network} 0.3921569 0.7843137
## [102] {perform} => {network} 0.3627451 0.7115385
## [103] {model} => {network} 0.3921569 0.7407407
## [104] {neural} => {network} 0.5882353 0.9677419
## [105] {network} => {neural} 0.5882353 0.8000000
## [106] {cnn,convolut} => {neural} 0.1078431 1.0000000
## [107] {cnn,neural} => {convolut} 0.1078431 0.9166667
## [108] {cnn,convolut} => {network} 0.1078431 1.0000000
## [109] {cnn,network} => {convolut} 0.1078431 0.9166667
## [110] {cnn,neural} => {network} 0.1176471 1.0000000
## [111] {cnn,network} => {neural} 0.1176471 1.0000000
## [112] {natur,neural} => {network} 0.1078431 1.0000000
## [113] {natur,network} => {neural} 0.1078431 0.9166667
## [114] {neural,addit} => {network} 0.1176471 1.0000000
## [115] {network,addit} => {neural} 0.1176471 0.9230769
## [116] {neural,introduc} => {network} 0.1176471 0.9230769
## [117] {network,introduc} => {neural} 0.1176471 0.8571429
## [118] {neural,experi} => {network} 0.1078431 1.0000000
## [119] {network,experi} => {neural} 0.1078431 0.7857143
## [120] {input,neural} => {network} 0.1176471 1.0000000
## [121] {input,network} => {neural} 0.1176471 0.8000000
## [122] {extract,featur} => {neural} 0.1078431 0.7857143
## [123] {neural,extract} => {featur} 0.1078431 0.7333333
## [124] {extract,featur} => {network} 0.1274510 0.9285714
## [125] {network,extract} => {featur} 0.1274510 0.8125000
## [126] {model,extract} => {neural} 0.1078431 0.9166667
## [127] {neural,extract} => {model} 0.1078431 0.7333333
## [128] {neural,extract} => {network} 0.1372549 0.9333333
## [129] {network,extract} => {neural} 0.1372549 0.8750000
## [130] {base,visual} => {network} 0.1176471 1.0000000
## [131] {network,visual} => {base} 0.1176471 0.7058824
## [132] {model,visual} => {neural} 0.1176471 0.8571429
## [133] {neural,visual} => {model} 0.1176471 0.9230769
## [134] {model,visual} => {network} 0.1372549 1.0000000
## [135] {network,visual} => {model} 0.1372549 0.8235294
## [136] {neural,visual} => {network} 0.1274510 1.0000000
## [137] {network,visual} => {neural} 0.1274510 0.7647059
## [138] {neural,high} => {network} 0.1078431 1.0000000
## [139] {network,high} => {neural} 0.1078431 0.7333333
## [140] {neural,challeng} => {network} 0.1274510 1.0000000
## [141] {network,challeng} => {neural} 0.1274510 0.8666667
## [142] {layer,neural} => {network} 0.1372549 0.9333333
## [143] {layer,network} => {neural} 0.1372549 1.0000000
## [144] {classif,classifi} => {neural} 0.1078431 0.7857143
## [145] {classif,classifi} => {network} 0.1274510 0.9285714
## [146] {achiev,classifi} => {network} 0.1078431 1.0000000
## [147] {featur,classifi} => {neural} 0.1078431 0.9166667
## [148] {featur,classifi} => {network} 0.1176471 1.0000000
## [149] {approach,classifi} => {neural} 0.1078431 0.8461538
## [150] {approach,classifi} => {network} 0.1274510 1.0000000
## [151] {convolut,classifi} => {network} 0.1176471 1.0000000
## [152] {neural,classifi} => {network} 0.1568627 1.0000000
## [153] {network,classifi} => {neural} 0.1568627 0.8421053
## [154] {convolut,time} => {network} 0.1078431 1.0000000
## [155] {network,time} => {convolut} 0.1078431 0.7333333
## [156] {neural,time} => {network} 0.1078431 1.0000000
## [157] {network,time} => {neural} 0.1078431 0.7333333
## [158] {neural,predict} => {network} 0.1176471 1.0000000
## [159] {network,predict} => {neural} 0.1176471 0.7500000
## [160] {recent,show} => {network} 0.1078431 0.9166667
## [161] {network,recent} => {show} 0.1078431 0.7333333
## [162] {model,recent} => {network} 0.1078431 0.8461538
## [163] {network,recent} => {model} 0.1078431 0.7333333
## [164] {neural,recent} => {network} 0.1176471 1.0000000
## [165] {network,recent} => {neural} 0.1176471 0.8000000
## [166] {combin,convolut} => {network} 0.1274510 1.0000000
## [167] {combin,result} => {neural} 0.1078431 0.9166667
## [168] {combin,paper} => {network} 0.1078431 0.9166667
## [169] {combin,neural} => {network} 0.1568627 0.9411765
## [170] {combin,network} => {neural} 0.1568627 0.8421053
## [171] {problem,result} => {paper} 0.1078431 0.9166667
## [172] {show,problem} => {paper} 0.1078431 0.9166667
## [173] {model,problem} => {paper} 0.1078431 1.0000000
## [174] {neural,problem} => {paper} 0.1176471 0.8000000
## [175] {paper,problem} => {network} 0.1372549 0.7777778
## [176] {network,problem} => {paper} 0.1372549 0.8235294
## [177] {neural,problem} => {network} 0.1470588 1.0000000
## [178] {network,problem} => {neural} 0.1470588 0.8823529
## [179] {propos,recognit} => {network} 0.1078431 0.9166667
## [180] {model,recognit} => {network} 0.1078431 0.7857143
## [181] {neural,recognit} => {network} 0.1176471 1.0000000
## [182] {network,recognit} => {neural} 0.1176471 0.7500000
## [183] {neural,appli} => {network} 0.1176471 1.0000000
## [184] {network,appli} => {neural} 0.1176471 0.8571429
## [185] {train,learn} => {network} 0.1078431 0.8461538
## [186] {model,learn} => {network} 0.1176471 0.8571429
## [187] {network,learn} => {model} 0.1176471 0.7500000
## [188] {neural,learn} => {network} 0.1274510 1.0000000
## [189] {network,learn} => {neural} 0.1274510 0.8125000
## [190] {comput,develop} => {network} 0.1176471 0.8571429
## [191] {network,develop} => {comput} 0.1176471 0.7500000
## [192] {model,develop} => {network} 0.1078431 0.9166667
## [193] {neural,develop} => {network} 0.1078431 0.9166667
## [194] {dataset,stateoftheart} => {train} 0.1078431 0.8461538
## [195] {train,stateoftheart} => {dataset} 0.1078431 0.7333333
## [196] {propos,stateoftheart} => {train} 0.1078431 0.7333333
## [197] {train,stateoftheart} => {propos} 0.1078431 0.7333333
## [198] {convolut,stateoftheart} => {network} 0.1078431 1.0000000
## [199] {neural,stateoftheart} => {network} 0.1078431 1.0000000
## [200] {convolut,system} => {network} 0.1078431 1.0000000
## [201] {neural,system} => {network} 0.1470588 1.0000000
## [202] {network,system} => {neural} 0.1470588 0.8823529
## [203] {neural,demonstr} => {network} 0.1372549 1.0000000
## [204] {network,demonstr} => {neural} 0.1372549 0.8750000
## [205] {set,larg} => {network} 0.1176471 1.0000000
## [206] {convolut,set} => {network} 0.1176471 1.0000000
## [207] {paper,set} => {network} 0.1078431 0.8461538
## [208] {train,set} => {network} 0.1078431 0.8461538
## [209] {model,set} => {network} 0.1078431 0.9166667
## [210] {neural,set} => {network} 0.1176471 1.0000000
## [211] {perform,effici} => {network} 0.1078431 0.7857143
## [212] {neural,effici} => {network} 0.1078431 1.0000000
## [213] {object,propos} => {network} 0.1078431 1.0000000
## [214] {object,train} => {network} 0.1078431 0.9166667
## [215] {neural,object} => {network} 0.1078431 0.9166667
## [216] {dataset,larg} => {network} 0.1176471 1.0000000
## [217] {approach,larg} => {network} 0.1078431 1.0000000
## [218] {propos,larg} => {network} 0.1078431 1.0000000
## [219] {convolut,larg} => {network} 0.1274510 1.0000000
## [220] {show,larg} => {neural} 0.1078431 0.7333333
## [221] {show,larg} => {network} 0.1470588 1.0000000
## [222] {train,larg} => {neural} 0.1078431 0.7857143
## [223] {train,larg} => {network} 0.1372549 1.0000000
## [224] {model,larg} => {network} 0.1274510 0.9285714
## [225] {neural,larg} => {network} 0.1568627 1.0000000
## [226] {network,larg} => {neural} 0.1568627 0.7272727
## [227] {show,evalu} => {network} 0.1078431 0.7333333
## [228] {network,evalu} => {show} 0.1078431 0.7333333
## [229] {neural,evalu} => {network} 0.1078431 0.9166667
## [230] {network,evalu} => {neural} 0.1078431 0.7333333
## [231] {neural,techniqu} => {network} 0.1372549 1.0000000
## [232] {network,techniqu} => {neural} 0.1372549 0.8235294
## [233] {compar,convolut} => {network} 0.1078431 0.9166667
## [234] {compar,result} => {perform} 0.1078431 0.7333333
## [235] {compar,train} => {perform} 0.1078431 0.7857143
## [236] {compar,perform} => {network} 0.1176471 0.7058824
## [237] {compar,network} => {perform} 0.1176471 0.7500000
## [238] {train,framework} => {perform} 0.1176471 0.8571429
## [239] {perform,framework} => {train} 0.1176471 0.7500000
## [240] {perform,framework} => {network} 0.1176471 0.7500000
## [241] {model,framework} => {neural} 0.1078431 0.7857143
## [242] {model,framework} => {network} 0.1176471 0.8571429
## [243] {neural,framework} => {network} 0.1666667 1.0000000
## [244] {network,framework} => {neural} 0.1666667 0.8500000
## [245] {applic,train} => {network} 0.1078431 0.7333333
## [246] {applic,neural} => {network} 0.1470588 1.0000000
## [247] {applic,network} => {neural} 0.1470588 0.9375000
## [248] {process,task} => {neural} 0.1078431 0.9166667
## [249] {process,task} => {network} 0.1078431 0.9166667
## [250] {perform,process} => {neural} 0.1176471 0.8571429
## [251] {neural,process} => {perform} 0.1176471 0.7058824
## [252] {perform,process} => {network} 0.1176471 0.8571429
## [253] {network,process} => {perform} 0.1176471 0.7058824
## [254] {neural,process} => {network} 0.1568627 0.9411765
## [255] {network,process} => {neural} 0.1568627 0.9411765
## [256] {imag,classif} => {network} 0.1078431 0.8461538
## [257] {convolut,classif} => {network} 0.1176471 0.9230769
## [258] {train,classif} => {network} 0.1274510 0.8666667
## [259] {model,classif} => {perform} 0.1078431 0.7333333
## [260] {perform,classif} => {network} 0.1176471 0.7500000
## [261] {model,classif} => {network} 0.1078431 0.7333333
## [262] {neural,classif} => {network} 0.1666667 0.9444444
## [263] {network,classif} => {neural} 0.1666667 0.8095238
## [264] {inform,neural} => {network} 0.1568627 1.0000000
## [265] {inform,network} => {neural} 0.1568627 0.8421053
## [266] {task,machin} => {neural} 0.1078431 0.8461538
## [267] {train,machin} => {perform} 0.1176471 0.8000000
## [268] {perform,machin} => {train} 0.1176471 0.7500000
## [269] {train,machin} => {network} 0.1176471 0.8000000
## [270] {perform,machin} => {network} 0.1274510 0.8125000
## [271] {model,machin} => {neural} 0.1078431 0.7857143
## [272] {model,machin} => {network} 0.1176471 0.8571429
## [273] {neural,machin} => {network} 0.1666667 0.9444444
## [274] {network,machin} => {neural} 0.1666667 0.8095238
## [275] {work,achiev} => {network} 0.1078431 1.0000000
## [276] {base,work} => {network} 0.1274510 0.9285714
## [277] {neural,work} => {network} 0.1470588 1.0000000
## [278] {network,work} => {neural} 0.1470588 0.7500000
## [279] {imag,present} => {neural} 0.1078431 0.9166667
## [280] {imag,present} => {network} 0.1176471 1.0000000
## [281] {present,achiev} => {network} 0.1176471 0.8571429
## [282] {present,data} => {network} 0.1078431 0.8461538
## [283] {convolut,present} => {neural} 0.1078431 0.8461538
## [284] {convolut,present} => {network} 0.1274510 1.0000000
## [285] {present,result} => {network} 0.1078431 0.7333333
## [286] {present,train} => {neural} 0.1470588 0.8333333
## [287] {neural,present} => {train} 0.1470588 0.7142857
## [288] {present,train} => {network} 0.1568627 0.8888889
## [289] {perform,present} => {network} 0.1176471 0.8000000
## [290] {model,present} => {neural} 0.1176471 0.8000000
## [291] {model,present} => {network} 0.1176471 0.8000000
## [292] {neural,present} => {network} 0.2058824 1.0000000
## [293] {network,present} => {neural} 0.2058824 0.8750000
## [294] {accuraci,improv} => {show} 0.1078431 0.8461538
## [295] {show,accuraci} => {improv} 0.1078431 0.7333333
## [296] {accuraci,achiev} => {neural} 0.1078431 0.8461538
## [297] {accuraci,achiev} => {network} 0.1274510 1.0000000
## [298] {accuraci,data} => {neural} 0.1176471 0.8571429
## [299] {accuraci,data} => {network} 0.1274510 0.9285714
## [300] {convolut,accuraci} => {network} 0.1176471 1.0000000
## [301] {accuraci,result} => {neural} 0.1176471 0.7058824
## [302] {accuraci,result} => {network} 0.1176471 0.7058824
## [303] {show,accuraci} => {network} 0.1176471 0.8000000
## [304] {model,accuraci} => {train} 0.1176471 0.8000000
## [305] {train,accuraci} => {neural} 0.1372549 0.7368421
## [306] {neural,accuraci} => {train} 0.1372549 0.7000000
## [307] {train,accuraci} => {network} 0.1470588 0.7894737
## [308] {perform,accuraci} => {network} 0.1176471 0.8571429
## [309] {model,accuraci} => {network} 0.1176471 0.8000000
## [310] {neural,accuraci} => {network} 0.1960784 1.0000000
## [311] {network,accuraci} => {neural} 0.1960784 0.8695652
## [312] {dataset,improv} => {show} 0.1078431 0.7857143
## [313] {dataset,improv} => {train} 0.1078431 0.7857143
## [314] {dataset,improv} => {network} 0.1078431 0.7857143
## [315] {imag,dataset} => {train} 0.1274510 0.8666667
## [316] {imag,dataset} => {network} 0.1274510 0.8666667
## [317] {dataset,propos} => {show} 0.1078431 0.7333333
## [318] {dataset,propos} => {train} 0.1078431 0.7333333
## [319] {convolut,dataset} => {network} 0.1372549 0.9333333
## [320] {dataset,result} => {train} 0.1078431 0.7333333
## [321] {show,dataset} => {train} 0.1568627 0.8421053
## [322] {show,dataset} => {network} 0.1470588 0.7894737
## [323] {perform,dataset} => {train} 0.1470588 0.8333333
## [324] {model,dataset} => {train} 0.1274510 0.7222222
## [325] {neural,dataset} => {train} 0.1372549 0.7777778
## [326] {train,dataset} => {network} 0.1764706 0.7826087
## [327] {network,dataset} => {train} 0.1764706 0.7826087
## [328] {model,dataset} => {network} 0.1274510 0.7222222
## [329] {neural,dataset} => {network} 0.1764706 1.0000000
## [330] {network,dataset} => {neural} 0.1764706 0.7826087
## [331] {improv,architectur} => {network} 0.1176471 0.8571429
## [332] {comput,architectur} => {network} 0.1078431 0.8461538
## [333] {convolut,architectur} => {network} 0.1274510 0.9285714
## [334] {architectur,result} => {perform} 0.1274510 0.7647059
## [335] {architectur,result} => {network} 0.1176471 0.7058824
## [336] {architectur,paper} => {network} 0.1078431 0.7857143
## [337] {show,architectur} => {neural} 0.1078431 0.8461538
## [338] {show,architectur} => {network} 0.1078431 0.8461538
## [339] {train,architectur} => {perform} 0.1274510 0.7647059
## [340] {train,architectur} => {neural} 0.1372549 0.8235294
## [341] {neural,architectur} => {train} 0.1372549 0.7000000
## [342] {train,architectur} => {network} 0.1666667 1.0000000
## [343] {perform,architectur} => {network} 0.1470588 0.7500000
## [344] {model,architectur} => {neural} 0.1176471 0.8571429
## [345] {model,architectur} => {network} 0.1078431 0.7857143
## [346] {neural,architectur} => {network} 0.1862745 0.9500000
## [347] {network,architectur} => {neural} 0.1862745 0.7600000
## [348] {comput,improv} => {network} 0.1078431 0.8461538
## [349] {convolut,improv} => {network} 0.1176471 1.0000000
## [350] {improv,result} => {perform} 0.1078431 0.7857143
## [351] {show,improv} => {neural} 0.1274510 0.7222222
## [352] {show,improv} => {network} 0.1372549 0.7777778
## [353] {train,improv} => {perform} 0.1470588 0.7500000
## [354] {perform,improv} => {train} 0.1470588 0.7894737
## [355] {train,improv} => {network} 0.1568627 0.8000000
## [356] {neural,improv} => {network} 0.1764706 0.9000000
## [357] {network,improv} => {neural} 0.1764706 0.7826087
## [358] {imag,task} => {network} 0.1078431 1.0000000
## [359] {base,imag} => {network} 0.1078431 0.9166667
## [360] {approach,imag} => {network} 0.1176471 1.0000000
## [361] {imag,comput} => {network} 0.1176471 0.8000000
## [362] {convolut,imag} => {network} 0.1862745 1.0000000
## [363] {imag,network} => {convolut} 0.1862745 0.7037037
## [364] {imag,method} => {network} 0.1078431 0.7857143
## [365] {imag,result} => {network} 0.1078431 0.7857143
## [366] {imag,paper} => {network} 0.1176471 0.8000000
## [367] {imag,show} => {network} 0.1176471 0.8571429
## [368] {imag,perform} => {train} 0.1176471 0.8000000
## [369] {imag,train} => {neural} 0.1470588 0.7142857
## [370] {imag,neural} => {train} 0.1470588 0.7500000
## [371] {imag,train} => {network} 0.1862745 0.9047619
## [372] {imag,network} => {train} 0.1862745 0.7037037
## [373] {imag,perform} => {network} 0.1372549 0.9333333
## [374] {imag,model} => {neural} 0.1176471 0.7500000
## [375] {imag,model} => {network} 0.1372549 0.8750000
## [376] {imag,neural} => {network} 0.1960784 1.0000000
## [377] {imag,network} => {neural} 0.1960784 0.7407407
## [378] {base,achiev} => {network} 0.1078431 0.9166667
## [379] {achiev,featur} => {network} 0.1274510 1.0000000
## [380] {achiev,propos} => {network} 0.1274510 0.9285714
## [381] {achiev,data} => {network} 0.1372549 0.8235294
## [382] {achiev,method} => {convolut} 0.1078431 0.7333333
## [383] {convolut,method} => {achiev} 0.1078431 0.7857143
## [384] {convolut,achiev} => {network} 0.1666667 1.0000000
## [385] {achiev,method} => {network} 0.1372549 0.9333333
## [386] {achiev,paper} => {network} 0.1372549 0.7368421
## [387] {train,achiev} => {neural} 0.1078431 0.7333333
## [388] {train,achiev} => {network} 0.1274510 0.8666667
## [389] {perform,achiev} => {network} 0.1470588 0.8333333
## [390] {model,achiev} => {network} 0.1274510 0.8125000
## [391] {neural,achiev} => {network} 0.1960784 0.9523810
## [392] {network,achiev} => {neural} 0.1960784 0.7142857
## [393] {base,task} => {neural} 0.1078431 0.8461538
## [394] {base,task} => {network} 0.1078431 0.8461538
## [395] {task,propos} => {network} 0.1078431 0.8461538
## [396] {convolut,task} => {network} 0.1078431 0.9166667
## [397] {task,paper} => {neural} 0.1274510 0.7647059
## [398] {task,paper} => {network} 0.1274510 0.7647059
## [399] {show,task} => {train} 0.1176471 0.7500000
## [400] {task,train} => {show} 0.1176471 0.7500000
## [401] {show,task} => {network} 0.1176471 0.7500000
## [402] {task,train} => {perform} 0.1176471 0.7500000
## [403] {task,train} => {network} 0.1274510 0.8125000
## [404] {model,task} => {neural} 0.1470588 0.7500000
## [405] {model,task} => {network} 0.1568627 0.8000000
## [406] {neural,task} => {network} 0.2058824 0.9130435
## [407] {network,task} => {neural} 0.2058824 0.8750000
## [408] {base,featur} => {network} 0.1372549 0.9333333
## [409] {approach,base} => {network} 0.1176471 1.0000000
## [410] {base,convolut} => {network} 0.1372549 1.0000000
## [411] {base,train} => {network} 0.1372549 0.7777778
## [412] {base,perform} => {network} 0.1274510 1.0000000
## [413] {base,model} => {neural} 0.1274510 0.7222222
## [414] {base,model} => {network} 0.1568627 0.8888889
## [415] {base,neural} => {network} 0.1862745 0.9500000
## [416] {approach,featur} => {network} 0.1372549 0.7777778
## [417] {propos,featur} => {model} 0.1078431 0.7333333
## [418] {propos,featur} => {neural} 0.1078431 0.7333333
## [419] {propos,featur} => {network} 0.1274510 0.8666667
## [420] {data,featur} => {network} 0.1078431 0.7857143
## [421] {convolut,featur} => {network} 0.1176471 1.0000000
## [422] {method,featur} => {paper} 0.1078431 0.7333333
## [423] {method,featur} => {model} 0.1176471 0.8000000
## [424] {method,featur} => {network} 0.1078431 0.7333333
## [425] {featur,result} => {model} 0.1078431 0.7333333
## [426] {featur,result} => {neural} 0.1078431 0.7333333
## [427] {featur,result} => {network} 0.1274510 0.8666667
## [428] {paper,featur} => {model} 0.1274510 0.7222222
## [429] {paper,featur} => {network} 0.1274510 0.7222222
## [430] {show,featur} => {neural} 0.1176471 0.7058824
## [431] {show,featur} => {network} 0.1470588 0.8823529
## [432] {train,featur} => {model} 0.1078431 0.7857143
## [433] {perform,featur} => {network} 0.1274510 0.7647059
## [434] {model,featur} => {neural} 0.1568627 0.7272727
## [435] {neural,featur} => {model} 0.1568627 0.7272727
## [436] {model,featur} => {network} 0.1764706 0.8181818
## [437] {neural,featur} => {network} 0.2156863 1.0000000
## [438] {network,featur} => {neural} 0.2156863 0.7857143
## [439] {approach,propos} => {network} 0.1372549 0.7777778
## [440] {approach,convolut} => {network} 0.1274510 0.9285714
## [441] {approach,result} => {network} 0.1176471 0.7058824
## [442] {approach,paper} => {show} 0.1176471 0.7058824
## [443] {approach,paper} => {network} 0.1274510 0.7647059
## [444] {approach,show} => {network} 0.1372549 0.7777778
## [445] {approach,train} => {network} 0.1372549 0.7368421
## [446] {approach,model} => {neural} 0.1176471 0.7500000
## [447] {approach,model} => {network} 0.1274510 0.8125000
## [448] {approach,neural} => {network} 0.2058824 1.0000000
## [449] {approach,network} => {neural} 0.2058824 0.7777778
## [450] {algorithm,comput} => {network} 0.1372549 0.8750000
## [451] {algorithm,data} => {perform} 0.1078431 0.8461538
## [452] {convolut,algorithm} => {network} 0.1078431 1.0000000
## [453] {train,algorithm} => {network} 0.1666667 0.8095238
## [454] {perform,algorithm} => {network} 0.1470588 0.7142857
## [455] {model,algorithm} => {network} 0.1274510 0.7647059
## [456] {neural,algorithm} => {network} 0.1960784 0.9523810
## [457] {network,algorithm} => {neural} 0.1960784 0.7692308
## [458] {convolut,comput} => {network} 0.1862745 0.9500000
## [459] {comput,result} => {network} 0.1176471 0.7500000
## [460] {comput,paper} => {network} 0.1470588 0.7500000
## [461] {show,comput} => {network} 0.1568627 0.8421053
## [462] {train,comput} => {neural} 0.1274510 0.7222222
## [463] {train,comput} => {network} 0.1470588 0.8333333
## [464] {perform,comput} => {network} 0.1372549 0.7777778
## [465] {model,comput} => {neural} 0.1176471 0.7058824
## [466] {model,comput} => {network} 0.1372549 0.8235294
## [467] {neural,comput} => {network} 0.2352941 1.0000000
## [468] {network,comput} => {neural} 0.2352941 0.8000000
## [469] {data,propos} => {network} 0.1274510 0.8125000
## [470] {convolut,propos} => {neural} 0.1176471 0.8000000
## [471] {convolut,propos} => {network} 0.1470588 1.0000000
## [472] {method,propos} => {network} 0.1372549 0.7000000
## [473] {propos,result} => {network} 0.1274510 0.7222222
## [474] {propos,paper} => {network} 0.1568627 0.7619048
## [475] {show,propos} => {network} 0.1568627 0.7619048
## [476] {perform,propos} => {train} 0.1372549 0.7000000
## [477] {train,propos} => {network} 0.1568627 0.7272727
## [478] {perform,propos} => {network} 0.1372549 0.7000000
## [479] {model,propos} => {network} 0.1568627 0.7619048
## [480] {neural,propos} => {network} 0.2352941 1.0000000
## [481] {network,propos} => {neural} 0.2352941 0.8275862
## [482] {convolut,data} => {network} 0.1274510 1.0000000
## [483] {data,method} => {perform} 0.1176471 0.7500000
## [484] {data,method} => {model} 0.1176471 0.7500000
## [485] {data,result} => {network} 0.1862745 0.7916667
## [486] {data,paper} => {perform} 0.1470588 0.7142857
## [487] {show,data} => {perform} 0.1372549 0.7000000
## [488] {show,data} => {network} 0.1568627 0.8000000
## [489] {train,data} => {neural} 0.1372549 0.7368421
## [490] {train,data} => {network} 0.1470588 0.7894737
## [491] {neural,data} => {network} 0.2254902 1.0000000
## [492] {network,data} => {neural} 0.2254902 0.7931034
## [493] {convolut,method} => {network} 0.1372549 1.0000000
## [494] {convolut,result} => {neural} 0.1372549 0.7777778
## [495] {convolut,result} => {network} 0.1666667 0.9444444
## [496] {convolut,paper} => {network} 0.2156863 1.0000000
## [497] {convolut,show} => {neural} 0.1372549 0.7000000
## [498] {convolut,show} => {network} 0.1960784 1.0000000
## [499] {convolut,train} => {neural} 0.1274510 0.7222222
## [500] {convolut,train} => {network} 0.1764706 1.0000000
## [501] {convolut,perform} => {network} 0.1862745 0.9500000
## [502] {convolut,model} => {neural} 0.1372549 0.7777778
## [503] {convolut,model} => {network} 0.1764706 1.0000000
## [504] {convolut,neural} => {network} 0.2647059 1.0000000
## [505] {convolut,network} => {neural} 0.2647059 0.7105263
## [506] {show,method} => {model} 0.1470588 0.7142857
## [507] {train,method} => {network} 0.1470588 0.7142857
## [508] {neural,method} => {network} 0.2058824 0.9545455
## [509] {network,method} => {neural} 0.2058824 0.7777778
## [510] {show,result} => {paper} 0.1764706 0.7500000
## [511] {paper,result} => {network} 0.1960784 0.7142857
## [512] {show,result} => {network} 0.1666667 0.7083333
## [513] {neural,result} => {network} 0.2745098 0.9655172
## [514] {network,result} => {neural} 0.2745098 0.8484848
## [515] {train,paper} => {network} 0.1666667 0.7391304
## [516] {neural,paper} => {network} 0.2549020 0.9285714
## [517] {network,paper} => {neural} 0.2549020 0.7647059
## [518] {show,train} => {network} 0.1862745 0.7600000
## [519] {perform,show} => {network} 0.1862745 0.7307692
## [520] {model,show} => {network} 0.2058824 0.7777778
## [521] {neural,show} => {network} 0.2941176 0.9677419
## [522] {network,show} => {neural} 0.2941176 0.7894737
## [523] {perform,train} => {network} 0.2450980 0.8064516
## [524] {model,train} => {network} 0.2156863 0.7586207
## [525] {neural,train} => {network} 0.3235294 1.0000000
## [526] {network,train} => {neural} 0.3235294 0.8250000
## [527] {neural,perform} => {network} 0.2549020 0.9629630
## [528] {network,perform} => {neural} 0.2549020 0.7027027
## [529] {model,neural} => {network} 0.3431373 0.9722222
## [530] {model,network} => {neural} 0.3431373 0.8750000
## [531] {cnn,convolut,neural} => {network} 0.1078431 1.0000000
## [532] {cnn,convolut,network} => {neural} 0.1078431 1.0000000
## [533] {cnn,network,neural} => {convolut} 0.1078431 0.9166667
## [534] {neural,extract,featur} => {network} 0.1078431 1.0000000
## [535] {network,extract,featur} => {neural} 0.1078431 0.8461538
## [536] {network,neural,extract} => {featur} 0.1078431 0.7857143
## [537] {model,neural,visual} => {network} 0.1176471 1.0000000
## [538] {model,network,visual} => {neural} 0.1176471 0.8571429
## [539] {network,neural,visual} => {model} 0.1176471 0.9230769
## [540] {neural,classif,classifi} => {network} 0.1078431 1.0000000
## [541] {network,classif,classifi} => {neural} 0.1078431 0.8461538
## [542] {neural,featur,classifi} => {network} 0.1078431 1.0000000
## [543] {network,featur,classifi} => {neural} 0.1078431 0.9166667
## [544] {approach,neural,classifi} => {network} 0.1078431 1.0000000
## [545] {approach,network,classifi} => {neural} 0.1078431 0.8461538
## [546] {neural,paper,problem} => {network} 0.1176471 1.0000000
## [547] {network,paper,problem} => {neural} 0.1176471 0.8571429
## [548] {network,neural,problem} => {paper} 0.1176471 0.8000000
## [549] {neural,show,larg} => {network} 0.1078431 1.0000000
## [550] {network,show,larg} => {neural} 0.1078431 0.7333333
## [551] {neural,train,larg} => {network} 0.1078431 1.0000000
## [552] {network,train,larg} => {neural} 0.1078431 0.7857143
## [553] {model,neural,framework} => {network} 0.1078431 1.0000000
## [554] {model,network,framework} => {neural} 0.1078431 0.9166667
## [555] {neural,perform,process} => {network} 0.1078431 0.9166667
## [556] {network,perform,process} => {neural} 0.1078431 0.9166667
## [557] {inform,model,neural} => {network} 0.1078431 1.0000000
## [558] {inform,model,network} => {neural} 0.1078431 1.0000000
## [559] {model,neural,machin} => {network} 0.1078431 1.0000000
## [560] {model,network,machin} => {neural} 0.1078431 0.9166667
## [561] {imag,neural,present} => {network} 0.1078431 1.0000000
## [562] {imag,network,present} => {neural} 0.1078431 0.9166667
## [563] {convolut,neural,present} => {network} 0.1078431 1.0000000
## [564] {convolut,network,present} => {neural} 0.1078431 0.8461538
## [565] {neural,present,train} => {network} 0.1470588 1.0000000
## [566] {network,present,train} => {neural} 0.1470588 0.9375000
## [567] {network,neural,present} => {train} 0.1470588 0.7142857
## [568] {model,neural,present} => {network} 0.1176471 1.0000000
## [569] {model,network,present} => {neural} 0.1176471 1.0000000
## [570] {neural,accuraci,achiev} => {network} 0.1078431 1.0000000
## [571] {network,accuraci,achiev} => {neural} 0.1078431 0.8461538
## [572] {neural,accuraci,data} => {network} 0.1176471 1.0000000
## [573] {network,accuraci,data} => {neural} 0.1176471 0.9230769
## [574] {neural,accuraci,result} => {network} 0.1176471 1.0000000
## [575] {network,accuraci,result} => {neural} 0.1176471 1.0000000
## [576] {neural,train,accuraci} => {network} 0.1372549 1.0000000
## [577] {network,train,accuraci} => {neural} 0.1372549 0.9333333
## [578] {network,neural,accuraci} => {train} 0.1372549 0.7000000
## [579] {imag,train,dataset} => {network} 0.1078431 0.8461538
## [580] {imag,network,dataset} => {train} 0.1078431 0.8461538
## [581] {perform,show,dataset} => {train} 0.1078431 1.0000000
## [582] {perform,train,dataset} => {show} 0.1078431 0.7333333
## [583] {show,train,dataset} => {network} 0.1176471 0.7500000
## [584] {network,show,dataset} => {train} 0.1176471 0.8000000
## [585] {neural,show,dataset} => {network} 0.1176471 1.0000000
## [586] {network,show,dataset} => {neural} 0.1176471 0.8000000
## [587] {perform,train,dataset} => {network} 0.1176471 0.8000000
## [588] {network,perform,dataset} => {train} 0.1176471 1.0000000
## [589] {neural,train,dataset} => {network} 0.1372549 1.0000000
## [590] {network,train,dataset} => {neural} 0.1372549 0.7777778
## [591] {network,neural,dataset} => {train} 0.1372549 0.7777778
## [592] {perform,train,architectur} => {network} 0.1274510 1.0000000
## [593] {network,train,architectur} => {perform} 0.1274510 0.7647059
## [594] {network,perform,architectur} => {train} 0.1274510 0.8666667
## [595] {neural,train,architectur} => {network} 0.1372549 1.0000000
## [596] {network,train,architectur} => {neural} 0.1372549 0.8235294
## [597] {network,neural,architectur} => {train} 0.1372549 0.7368421
## [598] {neural,perform,architectur} => {network} 0.1078431 0.9166667
## [599] {network,perform,architectur} => {neural} 0.1078431 0.7333333
## [600] {model,neural,architectur} => {network} 0.1078431 0.9166667
## [601] {model,network,architectur} => {neural} 0.1078431 1.0000000
## [602] {neural,show,improv} => {network} 0.1176471 0.9230769
## [603] {network,show,improv} => {neural} 0.1176471 0.8571429
## [604] {perform,train,improv} => {network} 0.1176471 0.8000000
## [605] {network,train,improv} => {perform} 0.1176471 0.7500000
## [606] {network,perform,improv} => {train} 0.1176471 0.9230769
## [607] {neural,train,improv} => {network} 0.1274510 1.0000000
## [608] {network,train,improv} => {neural} 0.1274510 0.8125000
## [609] {network,neural,improv} => {train} 0.1274510 0.7222222
## [610] {convolut,imag,train} => {network} 0.1176471 1.0000000
## [611] {convolut,imag,neural} => {network} 0.1274510 1.0000000
## [612] {imag,perform,train} => {network} 0.1078431 0.9166667
## [613] {imag,network,perform} => {train} 0.1078431 0.7857143
## [614] {imag,neural,train} => {network} 0.1470588 1.0000000
## [615] {imag,network,train} => {neural} 0.1470588 0.7894737
## [616] {imag,network,neural} => {train} 0.1470588 0.7500000
## [617] {imag,model,neural} => {network} 0.1176471 1.0000000
## [618] {imag,model,network} => {neural} 0.1176471 0.8571429
## [619] {convolut,achiev,method} => {network} 0.1078431 1.0000000
## [620] {network,achiev,method} => {convolut} 0.1078431 0.7857143
## [621] {convolut,network,method} => {achiev} 0.1078431 0.7857143
## [622] {convolut,achiev,paper} => {network} 0.1078431 1.0000000
## [623] {network,achiev,paper} => {convolut} 0.1078431 0.7857143
## [624] {convolut,neural,achiev} => {network} 0.1078431 1.0000000
## [625] {neural,achiev,paper} => {network} 0.1078431 0.9166667
## [626] {network,achiev,paper} => {neural} 0.1078431 0.7857143
## [627] {neural,train,achiev} => {network} 0.1078431 1.0000000
## [628] {network,train,achiev} => {neural} 0.1078431 0.8461538
## [629] {neural,task,paper} => {network} 0.1078431 0.8461538
## [630] {network,task,paper} => {neural} 0.1078431 0.8461538
## [631] {neural,task,train} => {network} 0.1078431 1.0000000
## [632] {network,task,train} => {neural} 0.1078431 0.8461538
## [633] {model,neural,task} => {network} 0.1372549 0.9333333
## [634] {model,network,task} => {neural} 0.1372549 0.8750000
## [635] {base,model,neural} => {network} 0.1274510 1.0000000
## [636] {base,model,network} => {neural} 0.1274510 0.8125000
## [637] {approach,neural,featur} => {network} 0.1078431 1.0000000
## [638] {approach,network,featur} => {neural} 0.1078431 0.7857143
## [639] {neural,propos,featur} => {network} 0.1078431 1.0000000
## [640] {network,propos,featur} => {neural} 0.1078431 0.8461538
## [641] {neural,featur,result} => {network} 0.1078431 1.0000000
## [642] {network,featur,result} => {neural} 0.1078431 0.8461538
## [643] {neural,show,featur} => {network} 0.1176471 1.0000000
## [644] {network,show,featur} => {neural} 0.1176471 0.8000000
## [645] {model,neural,featur} => {network} 0.1568627 1.0000000
## [646] {model,network,featur} => {neural} 0.1568627 0.8888889
## [647] {network,neural,featur} => {model} 0.1568627 0.7272727
## [648] {approach,model,neural} => {network} 0.1176471 1.0000000
## [649] {approach,model,network} => {neural} 0.1176471 0.9230769
## [650] {neural,algorithm,comput} => {network} 0.1078431 1.0000000
## [651] {network,algorithm,comput} => {neural} 0.1078431 0.7857143
## [652] {perform,train,algorithm} => {network} 0.1078431 0.7857143
## [653] {network,perform,algorithm} => {train} 0.1078431 0.7333333
## [654] {neural,train,algorithm} => {network} 0.1274510 1.0000000
## [655] {network,train,algorithm} => {neural} 0.1274510 0.7647059
## [656] {convolut,comput,paper} => {network} 0.1078431 1.0000000
## [657] {network,comput,paper} => {convolut} 0.1078431 0.7333333
## [658] {convolut,show,comput} => {network} 0.1176471 1.0000000
## [659] {network,show,comput} => {convolut} 0.1176471 0.7500000
## [660] {convolut,neural,comput} => {network} 0.1274510 1.0000000
## [661] {neural,comput,paper} => {network} 0.1078431 1.0000000
## [662] {network,comput,paper} => {neural} 0.1078431 0.7333333
## [663] {neural,show,comput} => {network} 0.1176471 1.0000000
## [664] {network,show,comput} => {neural} 0.1176471 0.7500000
## [665] {neural,train,comput} => {network} 0.1274510 1.0000000
## [666] {network,train,comput} => {neural} 0.1274510 0.8666667
## [667] {model,neural,comput} => {network} 0.1176471 1.0000000
## [668] {model,network,comput} => {neural} 0.1176471 0.8571429
## [669] {neural,data,propos} => {network} 0.1078431 1.0000000
## [670] {network,data,propos} => {neural} 0.1078431 0.8461538
## [671] {convolut,neural,propos} => {network} 0.1176471 1.0000000
## [672] {convolut,network,propos} => {neural} 0.1176471 0.8000000
## [673] {neural,method,propos} => {network} 0.1176471 1.0000000
## [674] {network,method,propos} => {neural} 0.1176471 0.8571429
## [675] {neural,propos,result} => {network} 0.1176471 1.0000000
## [676] {network,propos,result} => {neural} 0.1176471 0.9230769
## [677] {neural,propos,paper} => {network} 0.1274510 1.0000000
## [678] {network,propos,paper} => {neural} 0.1274510 0.8125000
## [679] {neural,show,propos} => {network} 0.1176471 1.0000000
## [680] {network,show,propos} => {neural} 0.1176471 0.7500000
## [681] {neural,train,propos} => {network} 0.1274510 1.0000000
## [682] {network,train,propos} => {neural} 0.1274510 0.8125000
## [683] {model,neural,propos} => {network} 0.1372549 1.0000000
## [684] {model,network,propos} => {neural} 0.1372549 0.8750000
## [685] {neural,data,result} => {network} 0.1568627 1.0000000
## [686] {network,data,result} => {neural} 0.1568627 0.8421053
## [687] {neural,show,data} => {network} 0.1078431 1.0000000
## [688] {neural,train,data} => {network} 0.1372549 1.0000000
## [689] {network,train,data} => {neural} 0.1372549 0.9333333
## [690] {neural,perform,data} => {network} 0.1176471 1.0000000
## [691] {network,perform,data} => {neural} 0.1176471 0.8000000
## [692] {model,neural,data} => {network} 0.1078431 1.0000000
## [693] {model,network,data} => {neural} 0.1078431 0.7857143
## [694] {convolut,paper,result} => {network} 0.1176471 1.0000000
## [695] {convolut,network,result} => {paper} 0.1176471 0.7058824
## [696] {convolut,neural,result} => {network} 0.1372549 1.0000000
## [697] {convolut,network,result} => {neural} 0.1372549 0.8235294
## [698] {convolut,show,paper} => {network} 0.1176471 1.0000000
## [699] {convolut,perform,paper} => {network} 0.1078431 1.0000000
## [700] {convolut,neural,paper} => {network} 0.1470588 1.0000000
## [701] {convolut,perform,show} => {network} 0.1176471 1.0000000
## [702] {convolut,model,show} => {network} 0.1078431 1.0000000
## [703] {convolut,neural,show} => {network} 0.1372549 1.0000000
## [704] {convolut,network,show} => {neural} 0.1372549 0.7000000
## [705] {convolut,neural,train} => {network} 0.1274510 1.0000000
## [706] {convolut,network,train} => {neural} 0.1274510 0.7222222
## [707] {convolut,model,neural} => {network} 0.1372549 1.0000000
## [708] {convolut,model,network} => {neural} 0.1372549 0.7777778
## [709] {neural,method,result} => {network} 0.1078431 0.9166667
## [710] {network,method,result} => {neural} 0.1078431 0.8461538
## [711] {perform,method,paper} => {model} 0.1078431 0.7333333
## [712] {model,perform,method} => {paper} 0.1078431 0.7333333
## [713] {neural,method,paper} => {network} 0.1176471 0.9230769
## [714] {network,method,paper} => {neural} 0.1176471 0.7500000
## [715] {perform,show,method} => {model} 0.1078431 0.7857143
## [716] {model,show,method} => {perform} 0.1078431 0.7333333
## [717] {model,perform,method} => {show} 0.1078431 0.7333333
## [718] {neural,train,method} => {network} 0.1274510 1.0000000
## [719] {network,train,method} => {neural} 0.1274510 0.8666667
## [720] {model,neural,method} => {network} 0.1274510 0.9285714
## [721] {model,network,method} => {neural} 0.1274510 0.8125000
## [722] {perform,paper,result} => {show} 0.1078431 0.7857143
## [723] {perform,show,result} => {paper} 0.1078431 0.7333333
## [724] {neural,show,result} => {paper} 0.1176471 0.7500000
## [725] {neural,show,paper} => {result} 0.1176471 0.8571429
## [726] {show,paper,result} => {network} 0.1274510 0.7222222
## [727] {network,show,result} => {paper} 0.1274510 0.7647059
## [728] {network,show,paper} => {result} 0.1274510 0.7222222
## [729] {neural,paper,result} => {network} 0.1666667 0.9444444
## [730] {network,paper,result} => {neural} 0.1666667 0.8500000
## [731] {perform,show,result} => {model} 0.1078431 0.7333333
## [732] {model,show,result} => {perform} 0.1078431 0.8461538
## [733] {model,perform,result} => {show} 0.1078431 0.7333333
## [734] {neural,show,result} => {network} 0.1470588 0.9375000
## [735] {network,show,result} => {neural} 0.1470588 0.8823529
## [736] {neural,train,result} => {network} 0.1372549 1.0000000
## [737] {network,train,result} => {neural} 0.1372549 0.8235294
## [738] {neural,perform,result} => {network} 0.1274510 0.9285714
## [739] {network,perform,result} => {neural} 0.1274510 0.7647059
## [740] {model,neural,result} => {network} 0.1372549 0.9333333
## [741] {model,network,result} => {neural} 0.1372549 0.9333333
## [742] {model,show,paper} => {perform} 0.1078431 0.7857143
## [743] {perform,show,paper} => {network} 0.1176471 0.7058824
## [744] {neural,show,paper} => {network} 0.1274510 0.9285714
## [745] {network,show,paper} => {neural} 0.1274510 0.7222222
## [746] {perform,train,paper} => {network} 0.1078431 0.7857143
## [747] {neural,train,paper} => {network} 0.1274510 1.0000000
## [748] {network,train,paper} => {neural} 0.1274510 0.7647059
## [749] {neural,perform,paper} => {network} 0.1078431 0.9166667
## [750] {model,neural,paper} => {network} 0.1568627 0.9411765
## [751] {model,network,paper} => {neural} 0.1568627 0.8888889
## [752] {perform,show,train} => {model} 0.1176471 0.7500000
## [753] {model,show,train} => {perform} 0.1176471 0.8000000
## [754] {perform,show,train} => {network} 0.1176471 0.7500000
## [755] {model,show,train} => {network} 0.1078431 0.7333333
## [756] {neural,show,train} => {network} 0.1568627 1.0000000
## [757] {network,show,train} => {neural} 0.1568627 0.8421053
## [758] {neural,perform,show} => {model} 0.1078431 0.7857143
## [759] {model,perform,show} => {network} 0.1274510 0.7222222
## [760] {neural,perform,show} => {network} 0.1274510 0.9285714
## [761] {model,neural,show} => {network} 0.1666667 0.9444444
## [762] {model,network,show} => {neural} 0.1666667 0.8095238
## [763] {neural,perform,train} => {model} 0.1274510 0.7222222
## [764] {model,neural,perform} => {train} 0.1274510 0.7647059
## [765] {model,perform,train} => {network} 0.1568627 0.8000000
## [766] {model,network,train} => {perform} 0.1568627 0.7272727
## [767] {model,network,perform} => {train} 0.1568627 0.8000000
## [768] {neural,perform,train} => {network} 0.1764706 1.0000000
## [769] {network,perform,train} => {neural} 0.1764706 0.7200000
## [770] {model,neural,train} => {network} 0.1862745 1.0000000
## [771] {model,network,train} => {neural} 0.1862745 0.8636364
## [772] {model,neural,perform} => {network} 0.1568627 0.9411765
## [773] {model,network,perform} => {neural} 0.1568627 0.8000000
## [774] {neural,show,paper,result} => {network} 0.1078431 0.9166667
## [775] {network,show,paper,result} => {neural} 0.1078431 0.8461538
## [776] {network,neural,show,result} => {paper} 0.1078431 0.7333333
## [777] {network,neural,show,paper} => {result} 0.1078431 0.8461538
## [778] {model,neural,perform,train} => {network} 0.1274510 1.0000000
## [779] {model,network,perform,train} => {neural} 0.1274510 0.8125000
## [780] {network,neural,perform,train} => {model} 0.1274510 0.7222222
## [781] {model,network,neural,perform} => {train} 0.1274510 0.8125000
## lift count
## [1] 1.7980769 11
## [2] 1.2466667 11
## [3] 1.3600000 12
## [4] 1.2466667 11
## [5] 1.8106509 12
## [6] 1.2466667 11
## [7] 1.2553846 12
## [8] 2.2130178 11
## [9] 1.5186104 12
## [10] 1.2553846 12
## [11] 0.9973333 11
## [12] 1.1786667 13
## [13] 1.4960000 11
## [14] 0.9973333 11
## [15] 1.2926267 11
## [16] 1.1657143 12
## [17] 1.4384615 11
## [18] 0.9973333 11
## [19] 2.0035714 11
## [20] 1.7142857 12
## [21] 1.5412088 11
## [22] 1.3161290 12
## [23] 1.1786667 13
## [24] 1.5000000 12
## [25] 1.4117647 12
## [26] 1.2580645 13
## [27] 1.1200000 14
## [28] 1.5111111 12
## [29] 1.2064516 11
## [30] 1.4400000 12
## [31] 1.3846154 12
## [32] 1.1200000 14
## [33] 1.4444444 13
## [34] 1.1612903 12
## [35] 1.2338710 12
## [36] 1.2750000 15
## [37] 2.3333333 14
## [38] 1.3709677 15
## [39] 1.2088889 16
## [40] 1.4117647 12
## [41] 1.3846154 12
## [42] 2.1818182 12
## [43] 1.5555556 14
## [44] 1.2580645 13
## [45] 1.3600000 17
## [46] 1.1333333 15
## [47] 1.4444444 13
## [48] 0.9600000 12
## [49] 1.0736842 15
## [50] 1.5000000 12
## [51] 1.4516129 15
## [52] 1.1200000 14
## [53] 2.5500000 14
## [54] 1.3161290 16
## [55] 1.2920000 19
## [56] 1.0736842 15
## [57] 1.0361905 16
## [58] 1.0736842 15
## [59] 1.3317972 17
## [60] 1.2304762 19
## [61] 1.7485714 18
## [62] 1.1751152 15
## [63] 1.1009524 17
## [64] 1.3918129 14
## [65] 1.1452632 16
## [66] 1.4571429 15
## [67] 1.3222222 14
## [68] 1.0880000 16
## [69] 1.9297297 14
## [70] 1.0880000 16
## [71] 1.0509091 17
## [72] 1.1234783 19
## [73] 1.0052174 17
## [74] 1.1009524 17
## [75] 1.3008696 22
## [76] 1.3894231 17
## [77] 1.0461538 20
## [78] 1.0200000 21
## [79] 0.9938462 19
## [80] 1.0577778 21
## [81] 1.0074074 20
## [82] 1.1913237 21
## [83] 1.1255172 24
## [84] 1.0786207 23
## [85] 1.4838710 23
## [86] 1.0090323 23
## [87] 1.0967742 25
## [88] 0.9775000 23
## [89] 1.1845161 27
## [90] 1.1539394 28
## [91] 0.9890909 24
## [92] 1.1539394 28
## [93] 1.1200000 28
## [94] 1.0200000 27
## [95] 0.9556757 26
## [96] 1.1027027 30
## [97] 1.0112821 29
## [98] 1.0112821 29
## [99] 1.3251282 38
## [100] 1.0766667 38
## [101] 1.0666667 40
## [102] 0.9676923 37
## [103] 1.0074074 40
## [104] 1.3161290 60
## [105] 1.3161290 60
## [106] 1.6451613 11
## [107] 2.3974359 11
## [108] 1.3600000 11
## [109] 2.3974359 11
## [110] 1.3600000 12
## [111] 1.6451613 12
## [112] 1.3600000 11
## [113] 1.5080645 11
## [114] 1.3600000 12
## [115] 1.5186104 12
## [116] 1.2553846 12
## [117] 1.4101382 12
## [118] 1.3600000 11
## [119] 1.2926267 11
## [120] 1.3600000 12
## [121] 1.3161290 12
## [122] 1.2926267 11
## [123] 2.2000000 11
## [124] 1.2628571 13
## [125] 2.4375000 13
## [126] 1.5080645 11
## [127] 1.3851852 11
## [128] 1.2693333 14
## [129] 1.4395161 14
## [130] 1.3600000 12
## [131] 2.1818182 12
## [132] 1.4101382 12
## [133] 1.7435897 12
## [134] 1.3600000 14
## [135] 1.5555556 14
## [136] 1.3600000 13
## [137] 1.2580645 13
## [138] 1.3600000 11
## [139] 1.2064516 11
## [140] 1.3600000 13
## [141] 1.4258065 13
## [142] 1.2693333 14
## [143] 1.6451613 14
## [144] 1.2926267 11
## [145] 1.2628571 13
## [146] 1.3600000 11
## [147] 1.5080645 11
## [148] 1.3600000 12
## [149] 1.3920596 11
## [150] 1.3600000 13
## [151] 1.3600000 12
## [152] 1.3600000 16
## [153] 1.3853990 16
## [154] 1.3600000 11
## [155] 1.9179487 11
## [156] 1.3600000 11
## [157] 1.2064516 11
## [158] 1.3600000 12
## [159] 1.2338710 12
## [160] 1.2466667 11
## [161] 1.5583333 11
## [162] 1.1507692 11
## [163] 1.3851852 11
## [164] 1.3600000 12
## [165] 1.3161290 12
## [166] 1.3600000 13
## [167] 1.5080645 11
## [168] 1.2466667 11
## [169] 1.2800000 16
## [170] 1.3853990 16
## [171] 1.8700000 11
## [172] 1.8700000 11
## [173] 2.0400000 11
## [174] 1.6320000 12
## [175] 1.0577778 14
## [176] 1.6800000 14
## [177] 1.3600000 15
## [178] 1.4516129 15
## [179] 1.2466667 11
## [180] 1.0685714 11
## [181] 1.3600000 12
## [182] 1.2338710 12
## [183] 1.3600000 12
## [184] 1.4101382 12
## [185] 1.1507692 11
## [186] 1.1657143 12
## [187] 1.4166667 12
## [188] 1.3600000 13
## [189] 1.3366935 13
## [190] 1.1657143 12
## [191] 2.0675676 12
## [192] 1.2466667 11
## [193] 1.2466667 11
## [194] 1.6923077 11
## [195] 2.4129032 11
## [196] 1.4666667 11
## [197] 1.9179487 11
## [198] 1.3600000 11
## [199] 1.3600000 11
## [200] 1.3600000 11
## [201] 1.3600000 15
## [202] 1.4516129 15
## [203] 1.3600000 14
## [204] 1.4395161 14
## [205] 1.3600000 12
## [206] 1.3600000 12
## [207] 1.1507692 11
## [208] 1.1507692 11
## [209] 1.2466667 11
## [210] 1.3600000 12
## [211] 1.0685714 11
## [212] 1.3600000 11
## [213] 1.3600000 11
## [214] 1.2466667 11
## [215] 1.2466667 11
## [216] 1.3600000 12
## [217] 1.3600000 11
## [218] 1.3600000 11
## [219] 1.3600000 13
## [220] 1.2064516 11
## [221] 1.3600000 15
## [222] 1.2926267 11
## [223] 1.3600000 14
## [224] 1.2628571 13
## [225] 1.3600000 16
## [226] 1.1964809 16
## [227] 0.9973333 11
## [228] 1.5583333 11
## [229] 1.2466667 11
## [230] 1.2064516 11
## [231] 1.3600000 14
## [232] 1.3548387 14
## [233] 1.2466667 11
## [234] 1.4384615 11
## [235] 1.5412088 11
## [236] 0.9600000 12
## [237] 1.4711538 12
## [238] 1.6813187 12
## [239] 1.5000000 12
## [240] 1.0200000 12
## [241] 1.2926267 11
## [242] 1.1657143 12
## [243] 1.3600000 17
## [244] 1.3983871 17
## [245] 0.9973333 11
## [246] 1.3600000 15
## [247] 1.5423387 15
## [248] 1.5080645 11
## [249] 1.2466667 11
## [250] 1.4101382 12
## [251] 1.3846154 12
## [252] 1.1657143 12
## [253] 1.3846154 12
## [254] 1.2800000 16
## [255] 1.5483871 16
## [256] 1.1507692 11
## [257] 1.2553846 12
## [258] 1.1786667 13
## [259] 1.4384615 11
## [260] 1.0200000 12
## [261] 0.9973333 11
## [262] 1.2844444 17
## [263] 1.3317972 17
## [264] 1.3600000 16
## [265] 1.3853990 16
## [266] 1.3920596 11
## [267] 1.5692308 12
## [268] 1.5000000 12
## [269] 1.0880000 12
## [270] 1.1050000 13
## [271] 1.2926267 11
## [272] 1.1657143 12
## [273] 1.2844444 17
## [274] 1.3317972 17
## [275] 1.3600000 11
## [276] 1.2628571 13
## [277] 1.3600000 15
## [278] 1.2338710 15
## [279] 1.5080645 11
## [280] 1.3600000 12
## [281] 1.1657143 12
## [282] 1.1507692 11
## [283] 1.3920596 11
## [284] 1.3600000 13
## [285] 0.9973333 11
## [286] 1.3709677 15
## [287] 1.4285714 15
## [288] 1.2088889 16
## [289] 1.0880000 12
## [290] 1.3161290 12
## [291] 1.0880000 12
## [292] 1.3600000 21
## [293] 1.4395161 21
## [294] 1.7980769 11
## [295] 2.3375000 11
## [296] 1.3920596 11
## [297] 1.3600000 13
## [298] 1.4101382 12
## [299] 1.2628571 13
## [300] 1.3600000 12
## [301] 1.1612903 12
## [302] 0.9600000 12
## [303] 1.0880000 12
## [304] 1.6000000 12
## [305] 1.2122241 14
## [306] 1.4000000 14
## [307] 1.0736842 15
## [308] 1.1657143 12
## [309] 1.0880000 12
## [310] 1.3600000 20
## [311] 1.4305750 20
## [312] 1.6696429 11
## [313] 1.5714286 11
## [314] 1.0685714 11
## [315] 1.7333333 13
## [316] 1.1786667 13
## [317] 1.5583333 11
## [318] 1.4666667 11
## [319] 1.2693333 14
## [320] 1.4666667 11
## [321] 1.6842105 16
## [322] 1.0736842 15
## [323] 1.6666667 15
## [324] 1.4444444 13
## [325] 1.5555556 14
## [326] 1.0643478 18
## [327] 1.5652174 18
## [328] 0.9822222 13
## [329] 1.3600000 18
## [330] 1.2875175 18
## [331] 1.1657143 12
## [332] 1.1507692 11
## [333] 1.2628571 13
## [334] 1.5000000 13
## [335] 0.9600000 12
## [336] 1.0685714 11
## [337] 1.3920596 11
## [338] 1.1507692 11
## [339] 1.5000000 13
## [340] 1.3548387 14
## [341] 1.4000000 14
## [342] 1.3600000 17
## [343] 1.0200000 15
## [344] 1.4101382 12
## [345] 1.0685714 11
## [346] 1.2920000 19
## [347] 1.2503226 19
## [348] 1.1507692 11
## [349] 1.3600000 12
## [350] 1.5412088 11
## [351] 1.1881720 13
## [352] 1.0577778 14
## [353] 1.4711538 15
## [354] 1.5789474 15
## [355] 1.0880000 16
## [356] 1.2240000 18
## [357] 1.2875175 18
## [358] 1.3600000 11
## [359] 1.2466667 11
## [360] 1.3600000 12
## [361] 1.0880000 12
## [362] 1.3600000 19
## [363] 1.8404558 19
## [364] 1.0685714 11
## [365] 1.0685714 11
## [366] 1.0880000 12
## [367] 1.1657143 12
## [368] 1.6000000 12
## [369] 1.1751152 15
## [370] 1.5000000 15
## [371] 1.2304762 19
## [372] 1.4074074 19
## [373] 1.2693333 14
## [374] 1.2338710 12
## [375] 1.1900000 14
## [376] 1.3600000 20
## [377] 1.2186380 20
## [378] 1.2466667 11
## [379] 1.3600000 13
## [380] 1.2628571 13
## [381] 1.1200000 14
## [382] 1.9179487 11
## [383] 2.4285714 11
## [384] 1.3600000 17
## [385] 1.2693333 14
## [386] 1.0021053 14
## [387] 1.2064516 11
## [388] 1.1786667 13
## [389] 1.1333333 15
## [390] 1.1050000 13
## [391] 1.2952381 20
## [392] 1.1751152 20
## [393] 1.3920596 11
## [394] 1.1507692 11
## [395] 1.1507692 11
## [396] 1.2466667 11
## [397] 1.2580645 13
## [398] 1.0400000 13
## [399] 1.5000000 12
## [400] 1.5937500 12
## [401] 1.0200000 12
## [402] 1.4711538 12
## [403] 1.1050000 13
## [404] 1.2338710 15
## [405] 1.0880000 16
## [406] 1.2417391 21
## [407] 1.4395161 21
## [408] 1.2693333 14
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## [410] 1.3600000 14
## [411] 1.0577778 14
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## [413] 1.1881720 13
## [414] 1.2088889 16
## [415] 1.2920000 19
## [416] 1.0577778 14
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## [420] 1.0685714 11
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## [423] 1.5111111 12
## [424] 0.9973333 11
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## [428] 1.3641975 13
## [429] 0.9822222 13
## [430] 1.1612903 12
## [431] 1.2000000 15
## [432] 1.4841270 11
## [433] 1.0400000 13
## [434] 1.1964809 16
## [435] 1.3737374 16
## [436] 1.1127273 18
## [437] 1.3600000 22
## [438] 1.2926267 22
## [439] 1.0577778 14
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## [444] 1.0577778 14
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## [446] 1.2338710 12
## [447] 1.1050000 13
## [448] 1.3600000 21
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## [450] 1.1900000 14
## [451] 1.6597633 11
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## [454] 0.9714286 15
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## [458] 1.2920000 19
## [459] 1.0200000 12
## [460] 1.0200000 15
## [461] 1.1452632 16
## [462] 1.1881720 13
## [463] 1.1333333 15
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## [469] 1.1050000 13
## [470] 1.3161290 12
## [471] 1.3600000 15
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## [482] 1.3600000 13
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## [489] 1.2122241 14
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## [491] 1.3600000 23
## [492] 1.3047831 23
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## [494] 1.2795699 14
## [495] 1.2844444 17
## [496] 1.3600000 22
## [497] 1.1516129 14
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## [499] 1.1881720 13
## [500] 1.3600000 18
## [501] 1.2920000 19
## [502] 1.2795699 14
## [503] 1.3600000 18
## [504] 1.3600000 27
## [505] 1.1689304 27
## [506] 1.3492063 15
## [507] 0.9714286 15
## [508] 1.2981818 21
## [509] 1.2795699 21
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## [511] 0.9714286 20
## [512] 0.9633333 17
## [513] 1.3131034 28
## [514] 1.3958944 28
## [515] 1.0052174 17
## [516] 1.2628571 26
## [517] 1.2580645 26
## [518] 1.0336000 19
## [519] 0.9938462 19
## [520] 1.0577778 21
## [521] 1.3161290 30
## [522] 1.2988115 30
## [523] 1.0967742 25
## [524] 1.0317241 22
## [525] 1.3600000 33
## [526] 1.3572581 33
## [527] 1.3096296 26
## [528] 1.1560593 26
## [529] 1.3222222 35
## [530] 1.4395161 35
## [531] 1.3600000 11
## [532] 1.6451613 11
## [533] 2.3974359 11
## [534] 1.3600000 11
## [535] 1.3920596 11
## [536] 2.3571429 11
## [537] 1.3600000 12
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## [540] 1.3600000 11
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## [589] 1.3600000 14
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## [593] 1.5000000 13
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## [596] 1.3548387 14
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## [598] 1.2466667 11
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## [600] 1.2466667 11
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## [602] 1.2553846 12
## [603] 1.4101382 12
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## [608] 1.3366935 13
## [609] 1.4444444 13
## [610] 1.3600000 12
## [611] 1.3600000 13
## [612] 1.2466667 11
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## [614] 1.3600000 15
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## [617] 1.3600000 12
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## [625] 1.2466667 11
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## [630] 1.3920596 11
## [631] 1.3600000 11
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## [633] 1.2693333 14
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## [635] 1.3600000 13
## [636] 1.3366935 13
## [637] 1.3600000 11
## [638] 1.2926267 11
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## [643] 1.3600000 12
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## [645] 1.3600000 16
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## [648] 1.3600000 12
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## [650] 1.3600000 11
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## [654] 1.3600000 13
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## [656] 1.3600000 11
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## [659] 1.9615385 12
## [660] 1.3600000 13
## [661] 1.3600000 11
## [662] 1.2064516 11
## [663] 1.3600000 12
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## [665] 1.3600000 13
## [666] 1.4258065 13
## [667] 1.3600000 12
## [668] 1.4101382 12
## [669] 1.3600000 11
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## [671] 1.3600000 12
## [672] 1.3161290 12
## [673] 1.3600000 12
## [674] 1.4101382 12
## [675] 1.3600000 12
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## [677] 1.3600000 13
## [678] 1.3366935 13
## [679] 1.3600000 12
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## [681] 1.3600000 13
## [682] 1.3366935 13
## [683] 1.3600000 14
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## [685] 1.3600000 16
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## [687] 1.3600000 11
## [688] 1.3600000 14
## [689] 1.5354839 14
## [690] 1.3600000 12
## [691] 1.3161290 12
## [692] 1.3600000 11
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## [699] 1.3600000 11
## [700] 1.3600000 15
## [701] 1.3600000 12
## [702] 1.3600000 11
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## [704] 1.1516129 14
## [705] 1.3600000 13
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## [707] 1.3600000 14
## [708] 1.2795699 14
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## [712] 1.4960000 11
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## [715] 1.4841270 11
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## [718] 1.3600000 13
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## [720] 1.2628571 13
## [721] 1.3366935 13
## [722] 1.6696429 11
## [723] 1.4960000 11
## [724] 1.5300000 12
## [725] 1.8214286 12
## [726] 0.9822222 13
## [727] 1.5600000 13
## [728] 1.5347222 13
## [729] 1.2844444 17
## [730] 1.3983871 17
## [731] 1.3851852 11
## [732] 1.6597633 11
## [733] 1.5583333 11
## [734] 1.2750000 15
## [735] 1.4516129 15
## [736] 1.3600000 14
## [737] 1.3548387 14
## [738] 1.2628571 13
## [739] 1.2580645 13
## [740] 1.2693333 14
## [741] 1.5354839 14
## [742] 1.5412088 11
## [743] 0.9600000 12
## [744] 1.2628571 13
## [745] 1.1881720 13
## [746] 1.0685714 11
## [747] 1.3600000 13
## [748] 1.2580645 13
## [749] 1.2466667 11
## [750] 1.2800000 16
## [751] 1.4623656 16
## [752] 1.4166667 12
## [753] 1.5692308 12
## [754] 1.0200000 12
## [755] 0.9973333 11
## [756] 1.3600000 16
## [757] 1.3853990 16
## [758] 1.4841270 11
## [759] 0.9822222 13
## [760] 1.2628571 13
## [761] 1.2844444 17
## [762] 1.3317972 17
## [763] 1.3641975 13
## [764] 1.5294118 13
## [765] 1.0880000 16
## [766] 1.4265734 16
## [767] 1.6000000 16
## [768] 1.3600000 18
## [769] 1.1845161 18
## [770] 1.3600000 19
## [771] 1.4208211 19
## [772] 1.2800000 16
## [773] 1.3161290 16
## [774] 1.2466667 11
## [775] 1.3920596 11
## [776] 1.4960000 11
## [777] 1.7980769 11
## [778] 1.3600000 13
## [779] 1.3366935 13
## [780] 1.3641975 13
## [781] 1.6250000 13
## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 23
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[3228 item(s), 237 transaction(s)] done [0.00s].
## sorting and recoding items ... [118 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [667 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support
## [1] {layer} => {network} 0.1181435
## [2] {estim} => {network} 0.1054852
## [3] {implement} => {network} 0.1054852
## [4] {success} => {network} 0.1054852
## [5] {experiment} => {result} 0.1139241
## [6] {experiment} => {network} 0.1012658
## [7] {increas} => {network} 0.1012658
## [8] {input} => {network} 0.1223629
## [9] {research} => {data} 0.1054852
## [10] {order} => {network} 0.1265823
## [11] {studi} => {network} 0.1054852
## [12] {cnn} => {convolut} 0.1350211
## [13] {cnn} => {neural} 0.1308017
## [14] {cnn} => {network} 0.1392405
## [15] {optim} => {network} 0.1097046
## [16] {time} => {network} 0.1223629
## [17] {complex} => {network} 0.1181435
## [18] {structur} => {network} 0.1223629
## [19] {outperform} => {network} 0.1308017
## [20] {human} => {network} 0.1181435
## [21] {effici} => {neural} 0.1223629
## [22] {effici} => {network} 0.1518987
## [23] {appli} => {network} 0.1392405
## [24] {techniqu} => {network} 0.1223629
## [25] {test} => {network} 0.1392405
## [26] {object} => {neural} 0.1392405
## [27] {object} => {network} 0.1476793
## [28] {multipl} => {network} 0.1350211
## [29] {process} => {network} 0.1350211
## [30] {experi} => {network} 0.1518987
## [31] {set} => {network} 0.1518987
## [32] {recent} => {network} 0.1434599
## [33] {improv} => {network} 0.1518987
## [34] {effect} => {network} 0.1476793
## [35] {predict} => {network} 0.1476793
## [36] {requir} => {network} 0.1392405
## [37] {present} => {network} 0.1561181
## [38] {learn} => {network} 0.1687764
## [39] {compar} => {network} 0.1772152
## [40] {develop} => {model} 0.1561181
## [41] {machin} => {neural} 0.1518987
## [42] {machin} => {network} 0.1603376
## [43] {framework} => {network} 0.1561181
## [44] {accuraci} => {network} 0.1645570
## [45] {classif} => {network} 0.2067511
## [46] {challeng} => {network} 0.1603376
## [47] {work} => {network} 0.1729958
## [48] {architectur} => {network} 0.2236287
## [49] {stateoftheart} => {network} 0.1898734
## [50] {applic} => {network} 0.1940928
## [51] {problem} => {network} 0.2109705
## [52] {demonstr} => {network} 0.2025316
## [53] {comput} => {network} 0.2067511
## [54] {achiev} => {network} 0.2194093
## [55] {featur} => {network} 0.2489451
## [56] {base} => {network} 0.2362869
## [57] {show} => {network} 0.2827004
## [58] {paper} => {network} 0.3248945
## [59] {method} => {network} 0.3037975
## [60] {convolut} => {neural} 0.3459916
## [61] {convolut} => {network} 0.3797468
## [62] {imag} => {network} 0.3291139
## [63] {approach} => {network} 0.3122363
## [64] {perform} => {network} 0.3122363
## [65] {propos} => {network} 0.3333333
## [66] {result} => {network} 0.3544304
## [67] {data} => {network} 0.3333333
## [68] {train} => {network} 0.3628692
## [69] {neural} => {network} 0.5738397
## [70] {network} => {neural} 0.5738397
## [71] {cnn,convolut} => {neural} 0.1308017
## [72] {neural,cnn} => {convolut} 0.1308017
## [73] {cnn,convolut} => {network} 0.1350211
## [74] {network,cnn} => {convolut} 0.1350211
## [75] {neural,cnn} => {network} 0.1308017
## [76] {network,cnn} => {neural} 0.1308017
## [77] {neural,recognit} => {network} 0.1054852
## [78] {network,recognit} => {neural} 0.1054852
## [79] {neural,outperform} => {network} 0.1054852
## [80] {network,outperform} => {neural} 0.1054852
## [81] {effici,neural} => {network} 0.1223629
## [82] {effici,network} => {neural} 0.1223629
## [83] {neural,appli} => {network} 0.1012658
## [84] {network,appli} => {neural} 0.1012658
## [85] {neural,techniqu} => {network} 0.1097046
## [86] {network,techniqu} => {neural} 0.1097046
## [87] {neural,test} => {network} 0.1223629
## [88] {network,test} => {neural} 0.1223629
## [89] {detect,neural} => {network} 0.1012658
## [90] {detect,network} => {neural} 0.1012658
## [91] {specif,neural} => {network} 0.1054852
## [92] {specif,network} => {neural} 0.1054852
## [93] {imag,object} => {neural} 0.1012658
## [94] {neural,object} => {imag} 0.1012658
## [95] {imag,object} => {network} 0.1012658
## [96] {neural,object} => {network} 0.1350211
## [97] {network,object} => {neural} 0.1350211
## [98] {multipl,neural} => {network} 0.1139241
## [99] {network,multipl} => {neural} 0.1139241
## [100] {neural,process} => {network} 0.1139241
## [101] {network,process} => {neural} 0.1139241
## [102] {experi,neural} => {network} 0.1350211
## [103] {network,experi} => {neural} 0.1350211
## [104] {set,neural} => {network} 0.1139241
## [105] {network,set} => {neural} 0.1139241
## [106] {neural,recent} => {network} 0.1223629
## [107] {network,recent} => {neural} 0.1223629
## [108] {improv,perform} => {network} 0.1097046
## [109] {improv,network} => {perform} 0.1097046
## [110] {improv,neural} => {network} 0.1223629
## [111] {improv,network} => {neural} 0.1223629
## [112] {effect,neural} => {network} 0.1181435
## [113] {network,effect} => {neural} 0.1181435
## [114] {neural,predict} => {network} 0.1223629
## [115] {network,predict} => {neural} 0.1223629
## [116] {neural,larg} => {network} 0.1139241
## [117] {network,larg} => {neural} 0.1139241
## [118] {requir,neural} => {network} 0.1223629
## [119] {network,requir} => {neural} 0.1223629
## [120] {neural,present} => {network} 0.1139241
## [121] {network,present} => {neural} 0.1139241
## [122] {learn,neural} => {network} 0.1350211
## [123] {learn,network} => {neural} 0.1350211
## [124] {result,compar} => {network} 0.1139241
## [125] {neural,compar} => {network} 0.1308017
## [126] {network,compar} => {neural} 0.1308017
## [127] {neural,develop} => {network} 0.1265823
## [128] {network,develop} => {neural} 0.1265823
## [129] {neural,machin} => {network} 0.1476793
## [130] {network,machin} => {neural} 0.1476793
## [131] {propos,framework} => {network} 0.1054852
## [132] {neural,framework} => {network} 0.1223629
## [133] {network,framework} => {neural} 0.1223629
## [134] {model,accuraci} => {network} 0.1054852
## [135] {neural,accuraci} => {network} 0.1265823
## [136] {network,accuraci} => {neural} 0.1265823
## [137] {classif,paper} => {network} 0.1097046
## [138] {classif,convolut} => {neural} 0.1097046
## [139] {classif,convolut} => {network} 0.1139241
## [140] {imag,classif} => {network} 0.1054852
## [141] {propos,classif} => {network} 0.1012658
## [142] {classif,data} => {network} 0.1054852
## [143] {train,classif} => {neural} 0.1012658
## [144] {train,classif} => {network} 0.1139241
## [145] {classif,neural} => {network} 0.1729958
## [146] {classif,network} => {neural} 0.1729958
## [147] {challeng,convolut} => {network} 0.1012658
## [148] {challeng,neural} => {network} 0.1350211
## [149] {challeng,network} => {neural} 0.1350211
## [150] {work,neural} => {network} 0.1434599
## [151] {work,network} => {neural} 0.1434599
## [152] {task,convolut} => {network} 0.1097046
## [153] {propos,task} => {network} 0.1097046
## [154] {neural,task} => {network} 0.1476793
## [155] {network,task} => {neural} 0.1476793
## [156] {architectur,paper} => {network} 0.1097046
## [157] {architectur,convolut} => {neural} 0.1054852
## [158] {architectur,convolut} => {network} 0.1265823
## [159] {imag,architectur} => {network} 0.1012658
## [160] {approach,architectur} => {network} 0.1139241
## [161] {propos,architectur} => {network} 0.1012658
## [162] {result,architectur} => {network} 0.1054852
## [163] {train,architectur} => {network} 0.1054852
## [164] {architectur,neural} => {network} 0.1603376
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## [526] {method,data,neural} => {network} 0.1350211
## [527] {method,data,network} => {neural} 0.1350211
## [528] {method,train,neural} => {network} 0.1350211
## [529] {method,train,network} => {neural} 0.1350211
## [530] {method,model,neural} => {network} 0.1139241
## [531] {method,model,network} => {neural} 0.1139241
## [532] {imag,approach,convolut} => {neural} 0.1181435
## [533] {imag,approach,neural} => {convolut} 0.1181435
## [534] {imag,approach,convolut} => {network} 0.1223629
## [535] {imag,network,approach} => {convolut} 0.1223629
## [536] {imag,perform,convolut} => {network} 0.1139241
## [537] {imag,perform,network} => {convolut} 0.1139241
## [538] {imag,propos,convolut} => {network} 0.1054852
## [539] {imag,result,convolut} => {network} 0.1139241
## [540] {imag,result,network} => {convolut} 0.1139241
## [541] {imag,data,convolut} => {neural} 0.1097046
## [542] {imag,data,neural} => {convolut} 0.1097046
## [543] {imag,data,convolut} => {network} 0.1139241
## [544] {imag,train,convolut} => {neural} 0.1054852
## [545] {imag,train,convolut} => {network} 0.1054852
## [546] {imag,neural,convolut} => {network} 0.1983122
## [547] {imag,network,convolut} => {neural} 0.1983122
## [548] {imag,network,neural} => {convolut} 0.1983122
## [549] {perform,approach,convolut} => {network} 0.1012658
## [550] {perform,network,approach} => {convolut} 0.1012658
## [551] {propos,approach,convolut} => {neural} 0.1054852
## [552] {propos,approach,neural} => {convolut} 0.1054852
## [553] {propos,approach,convolut} => {network} 0.1139241
## [554] {result,approach,convolut} => {network} 0.1054852
## [555] {data,approach,convolut} => {neural} 0.1097046
## [556] {data,approach,neural} => {convolut} 0.1097046
## [557] {data,approach,convolut} => {network} 0.1223629
## [558] {approach,neural,convolut} => {network} 0.1898734
## [559] {network,approach,convolut} => {neural} 0.1898734
## [560] {network,approach,neural} => {convolut} 0.1898734
## [561] {perform,propos,convolut} => {network} 0.1012658
## [562] {perform,neural,convolut} => {network} 0.1518987
## [563] {perform,network,convolut} => {neural} 0.1518987
## [564] {propos,result,convolut} => {network} 0.1054852
## [565] {propos,neural,convolut} => {network} 0.1518987
## [566] {propos,network,convolut} => {neural} 0.1518987
## [567] {result,neural,convolut} => {network} 0.1645570
## [568] {result,network,convolut} => {neural} 0.1645570
## [569] {train,data,convolut} => {network} 0.1012658
## [570] {data,neural,convolut} => {network} 0.1645570
## [571] {data,network,convolut} => {neural} 0.1645570
## [572] {train,neural,convolut} => {network} 0.1518987
## [573] {train,network,convolut} => {neural} 0.1518987
## [574] {model,neural,convolut} => {network} 0.1603376
## [575] {model,network,convolut} => {neural} 0.1603376
## [576] {imag,approach,neural} => {network} 0.1223629
## [577] {imag,network,approach} => {neural} 0.1223629
## [578] {imag,perform,neural} => {network} 0.1265823
## [579] {imag,perform,network} => {neural} 0.1265823
## [580] {imag,propos,neural} => {network} 0.1223629
## [581] {imag,propos,network} => {neural} 0.1223629
## [582] {imag,result,neural} => {network} 0.1139241
## [583] {imag,result,network} => {neural} 0.1139241
## [584] {imag,data,neural} => {network} 0.1518987
## [585] {imag,data,network} => {neural} 0.1518987
## [586] {imag,train,neural} => {network} 0.1434599
## [587] {imag,train,network} => {neural} 0.1434599
## [588] {imag,model,neural} => {network} 0.1054852
## [589] {imag,model,network} => {neural} 0.1054852
## [590] {perform,propos,approach} => {network} 0.1054852
## [591] {perform,network,approach} => {propos} 0.1054852
## [592] {perform,approach,neural} => {network} 0.1097046
## [593] {perform,network,approach} => {neural} 0.1097046
## [594] {propos,result,approach} => {network} 0.1054852
## [595] {propos,data,approach} => {network} 0.1097046
## [596] {propos,approach,neural} => {network} 0.1350211
## [597] {propos,network,approach} => {neural} 0.1350211
## [598] {result,approach,neural} => {network} 0.1223629
## [599] {result,network,approach} => {neural} 0.1223629
## [600] {data,approach,neural} => {network} 0.1392405
## [601] {data,network,approach} => {neural} 0.1392405
## [602] {train,approach,neural} => {network} 0.1097046
## [603] {train,network,approach} => {neural} 0.1097046
## [604] {model,approach,neural} => {network} 0.1350211
## [605] {model,network,approach} => {neural} 0.1350211
## [606] {perform,propos,neural} => {network} 0.1223629
## [607] {perform,propos,network} => {neural} 0.1223629
## [608] {perform,result,neural} => {network} 0.1097046
## [609] {perform,result,network} => {neural} 0.1097046
## [610] {perform,data,neural} => {network} 0.1308017
## [611] {perform,data,network} => {neural} 0.1308017
## [612] {perform,train,neural} => {network} 0.1265823
## [613] {perform,train,network} => {neural} 0.1265823
## [614] {model,perform,neural} => {network} 0.1139241
## [615] {model,perform,network} => {neural} 0.1139241
## [616] {propos,result,neural} => {network} 0.1223629
## [617] {propos,result,network} => {neural} 0.1223629
## [618] {propos,data,neural} => {network} 0.1265823
## [619] {propos,data,network} => {neural} 0.1265823
## [620] {propos,train,neural} => {network} 0.1265823
## [621] {propos,train,network} => {neural} 0.1265823
## [622] {model,propos,neural} => {network} 0.1350211
## [623] {model,propos,network} => {neural} 0.1350211
## [624] {result,data,neural} => {network} 0.1308017
## [625] {result,data,network} => {neural} 0.1308017
## [626] {result,train,neural} => {network} 0.1265823
## [627] {result,train,network} => {neural} 0.1265823
## [628] {model,result,neural} => {network} 0.1223629
## [629] {model,result,network} => {neural} 0.1223629
## [630] {train,data,neural} => {network} 0.1603376
## [631] {train,data,network} => {neural} 0.1603376
## [632] {model,data,neural} => {network} 0.1350211
## [633] {model,data,network} => {neural} 0.1350211
## [634] {model,train,neural} => {network} 0.1350211
## [635] {model,train,network} => {neural} 0.1350211
## [636] {result,show,neural,convolut} => {network} 0.1012658
## [637] {result,show,network,convolut} => {neural} 0.1012658
## [638] {imag,method,neural,convolut} => {network} 0.1392405
## [639] {imag,method,network,convolut} => {neural} 0.1392405
## [640] {method,network,neural,convolut} => {imag} 0.1392405
## [641] {imag,method,network,neural} => {convolut} 0.1392405
## [642] {imag,network,neural,convolut} => {method} 0.1392405
## [643] {method,approach,neural,convolut} => {network} 0.1012658
## [644] {method,network,approach,convolut} => {neural} 0.1012658
## [645] {method,network,approach,neural} => {convolut} 0.1012658
## [646] {method,data,neural,convolut} => {network} 0.1097046
## [647] {method,data,network,convolut} => {neural} 0.1097046
## [648] {method,data,network,neural} => {convolut} 0.1097046
## [649] {imag,method,data,neural} => {network} 0.1012658
## [650] {imag,method,data,network} => {neural} 0.1012658
## [651] {method,data,network,neural} => {imag} 0.1012658
## [652] {imag,method,train,neural} => {network} 0.1012658
## [653] {imag,method,train,network} => {neural} 0.1012658
## [654] {method,train,network,neural} => {imag} 0.1012658
## [655] {imag,train,network,neural} => {method} 0.1012658
## [656] {imag,approach,neural,convolut} => {network} 0.1139241
## [657] {imag,network,approach,convolut} => {neural} 0.1139241
## [658] {imag,network,approach,neural} => {convolut} 0.1139241
## [659] {imag,data,neural,convolut} => {network} 0.1097046
## [660] {imag,data,network,convolut} => {neural} 0.1097046
## [661] {imag,data,network,neural} => {convolut} 0.1097046
## [662] {propos,approach,neural,convolut} => {network} 0.1012658
## [663] {propos,network,approach,convolut} => {neural} 0.1012658
## [664] {propos,network,approach,neural} => {convolut} 0.1012658
## [665] {data,approach,neural,convolut} => {network} 0.1097046
## [666] {data,network,approach,convolut} => {neural} 0.1097046
## [667] {data,network,approach,neural} => {convolut} 0.1097046
## confidence lift count
## [1] 0.8484848 1.1759702 28
## [2] 0.8333333 1.1549708 25
## [3] 0.8064516 1.1177136 25
## [4] 0.7575758 1.0499734 25
## [5] 0.8437500 1.8515625 27
## [6] 0.7500000 1.0394737 24
## [7] 0.7741935 1.0730051 24
## [8] 0.8285714 1.1483709 29
## [9] 0.7352941 1.6135621 25
## [10] 0.8333333 1.1549708 30
## [11] 0.7142857 0.9899749 25
## [12] 0.9142857 2.3051672 32
## [13] 0.8857143 1.4993878 31
## [14] 0.9428571 1.3067669 33
## [15] 0.7647059 1.0598555 26
## [16] 0.8055556 1.1164717 29
## [17] 0.7777778 1.0779727 28
## [18] 0.7631579 1.0577101 29
## [19] 0.7750000 1.0741228 31
## [20] 0.7179487 0.9950517 28
## [21] 0.7073171 1.1973868 29
## [22] 0.8780488 1.2169448 36
## [23] 0.7674419 1.0636475 33
## [24] 0.7250000 1.0048246 29
## [25] 0.7674419 1.0636475 33
## [26] 0.7173913 1.2144410 33
## [27] 0.7608696 1.0545385 35
## [28] 0.7111111 0.9855750 32
## [29] 0.7441860 1.0314157 32
## [30] 0.7346939 1.0182599 36
## [31] 0.7500000 1.0394737 36
## [32] 0.7555556 1.0471735 34
## [33] 0.7346939 1.0182599 36
## [34] 0.7608696 1.0545385 35
## [35] 0.7446809 1.0321015 35
## [36] 0.7333333 1.0163743 33
## [37] 0.7115385 0.9861673 37
## [38] 0.7547170 1.0460113 40
## [39] 0.8571429 1.1879699 42
## [40] 0.7254902 1.4571286 37
## [41] 0.7058824 1.1949580 36
## [42] 0.7450980 1.0326797 38
## [43] 0.7254902 1.0055040 37
## [44] 0.7222222 1.0009747 39
## [45] 0.8305085 1.1510556 49
## [46] 0.7169811 0.9937107 38
## [47] 0.7321429 1.0147243 41
## [48] 0.8281250 1.1477522 53
## [49] 0.7758621 1.0753176 45
## [50] 0.7666667 1.0625731 46
## [51] 0.7692308 1.0661269 50
## [52] 0.7384615 1.0234818 48
## [53] 0.7424242 1.0289740 49
## [54] 0.7222222 1.0009747 52
## [55] 0.7564103 1.0483581 59
## [56] 0.7000000 0.9701754 56
## [57] 0.7444444 1.0317739 67
## [58] 0.7938144 1.1001990 77
## [59] 0.7422680 1.0287575 72
## [60] 0.8723404 1.4767477 82
## [61] 0.9574468 1.3269877 90
## [62] 0.8125000 1.1260965 78
## [63] 0.7708333 1.0683480 74
## [64] 0.7254902 1.0055040 74
## [65] 0.7596154 1.0528003 79
## [66] 0.7777778 1.0779727 84
## [67] 0.7314815 1.0138077 79
## [68] 0.7678571 1.0642231 86
## [69] 0.9714286 1.3463659 136
## [70] 0.7953216 1.3463659 136
## [71] 0.9687500 1.6399554 31
## [72] 1.0000000 2.5212766 31
## [73] 1.0000000 1.3859649 32
## [74] 0.9696970 2.4448743 32
## [75] 1.0000000 1.3859649 31
## [76] 0.9393939 1.5902597 31
## [77] 0.9615385 1.3326586 25
## [78] 0.9259259 1.5674603 25
## [79] 0.9259259 1.2833008 25
## [80] 0.8064516 1.3652074 25
## [81] 1.0000000 1.3859649 29
## [82] 0.8055556 1.3636905 29
## [83] 0.9600000 1.3305263 24
## [84] 0.7272727 1.2311688 24
## [85] 1.0000000 1.3859649 26
## [86] 0.8965517 1.5177340 26
## [87] 1.0000000 1.3859649 29
## [88] 0.8787879 1.4876623 29
## [89] 1.0000000 1.3859649 24
## [90] 0.7741935 1.3105991 24
## [91] 0.9259259 1.2833008 25
## [92] 0.9259259 1.5674603 25
## [93] 0.8000000 1.3542857 24
## [94] 0.7272727 1.7954545 24
## [95] 0.8000000 1.1087719 24
## [96] 0.9696970 1.3439660 32
## [97] 0.9142857 1.5477551 32
## [98] 1.0000000 1.3859649 27
## [99] 0.8437500 1.4283482 27
## [100] 1.0000000 1.3859649 27
## [101] 0.8437500 1.4283482 27
## [102] 1.0000000 1.3859649 32
## [103] 0.8888889 1.5047619 32
## [104] 0.9642857 1.3364662 27
## [105] 0.7500000 1.2696429 27
## [106] 1.0000000 1.3859649 29
## [107] 0.8529412 1.4439076 29
## [108] 0.7878788 1.0919724 26
## [109] 0.7222222 1.6781046 26
## [110] 0.9666667 1.3397661 29
## [111] 0.8055556 1.3636905 29
## [112] 1.0000000 1.3859649 28
## [113] 0.8000000 1.3542857 28
## [114] 0.9666667 1.3397661 29
## [115] 0.8285714 1.4026531 29
## [116] 1.0000000 1.3859649 27
## [117] 0.9000000 1.5235714 27
## [118] 1.0000000 1.3859649 29
## [119] 0.8787879 1.4876623 29
## [120] 1.0000000 1.3859649 27
## [121] 0.7297297 1.2353282 27
## [122] 0.9696970 1.3439660 32
## [123] 0.8000000 1.3542857 32
## [124] 0.9310345 1.2903811 27
## [125] 1.0000000 1.3859649 31
## [126] 0.7380952 1.2494898 31
## [127] 1.0000000 1.3859649 30
## [128] 0.9090909 1.5389610 30
## [129] 0.9722222 1.3474659 35
## [130] 0.9210526 1.5592105 35
## [131] 0.7812500 1.0827851 25
## [132] 0.9354839 1.2965478 29
## [133] 0.7837838 1.3268340 29
## [134] 0.8064516 1.1177136 25
## [135] 0.9677419 1.3412564 30
## [136] 0.7692308 1.3021978 30
## [137] 0.8666667 1.2011696 26
## [138] 0.9629630 1.6301587 26
## [139] 1.0000000 1.3859649 27
## [140] 0.8620690 1.1947973 25
## [141] 0.8888889 1.2319688 24
## [142] 0.7812500 1.0827851 25
## [143] 0.8275862 1.4009852 24
## [144] 0.9310345 1.2903811 27
## [145] 1.0000000 1.3859649 41
## [146] 0.8367347 1.4164723 41
## [147] 1.0000000 1.3859649 24
## [148] 1.0000000 1.3859649 32
## [149] 0.8421053 1.4255639 32
## [150] 0.9714286 1.3463659 34
## [151] 0.8292683 1.4038328 34
## [152] 1.0000000 1.3859649 26
## [153] 0.7428571 1.0295739 26
## [154] 1.0000000 1.3859649 35
## [155] 0.8536585 1.4451220 35
## [156] 0.8666667 1.2011696 26
## [157] 0.8064516 1.3652074 25
## [158] 0.9677419 1.3412564 30
## [159] 0.8571429 1.1879699 24
## [160] 0.9000000 1.2473684 27
## [161] 0.8888889 1.2319688 24
## [162] 0.9259259 1.2833008 25
## [163] 0.8928571 1.2374687 25
## [164] 1.0000000 1.3859649 38
## [165] 0.7169811 1.2137466 38
## [166] 0.9615385 1.3326586 25
## [167] 0.8064516 1.1177136 25
## [168] 0.8064516 1.1177136 25
## [169] 0.8181818 1.1339713 27
## [170] 0.8333333 1.1549708 30
## [171] 0.7500000 1.0394737 24
## [172] 0.9729730 1.3485064 36
## [173] 0.8000000 1.3542857 36
## [174] 0.7812500 1.0827851 25
## [175] 0.7058824 0.9783282 24
## [176] 1.0000000 1.3859649 37
## [177] 0.8043478 1.3616460 37
## [178] 0.9696970 1.3439660 32
## [179] 0.7804878 1.3212544 32
## [180] 0.7027027 1.1895753 26
## [181] 0.8108108 1.1237553 30
## [182] 0.8888889 1.5047619 24
## [183] 0.9259259 1.2833008 25
## [184] 0.7352941 1.6756222 25
## [185] 0.7058824 1.1949580 24
## [186] 0.8235294 1.1413829 28
## [187] 0.7567568 1.0488383 28
## [188] 0.8611111 1.1934698 31
## [189] 0.9761905 1.3529657 41
## [190] 0.8200000 1.3881429 41
## [191] 0.7500000 1.0394737 27
## [192] 0.9411765 1.3044376 32
## [193] 0.7272727 1.2311688 32
## [194] 0.8064516 1.1177136 25
## [195] 0.8000000 1.1087719 24
## [196] 0.7631579 1.2919173 29
## [197] 0.7073171 1.4967334 29
## [198] 0.7631579 1.0577101 29
## [199] 0.9268293 1.2845528 38
## [200] 0.7916667 1.3401786 38
## [201] 1.0000000 1.6928571 27
## [202] 0.9629630 1.3346329 26
## [203] 0.7500000 1.2696429 24
## [204] 0.7812500 1.0827851 25
## [205] 0.7500000 1.2696429 24
## [206] 0.8750000 1.2127193 28
## [207] 0.7575758 1.0499734 25
## [208] 0.7272727 1.0079745 24
## [209] 0.7027027 1.1895753 26
## [210] 0.7837838 1.0862968 29
## [211] 0.9777778 1.3551657 44
## [212] 0.8979592 1.5201166 44
## [213] 0.8387097 1.4198157 26
## [214] 0.9677419 1.3412564 30
## [215] 0.7894737 1.0941828 30
## [216] 0.7750000 1.0741228 31
## [217] 0.7027027 0.9739213 26
## [218] 0.7714286 1.0691729 27
## [219] 0.8333333 1.1549708 30
## [220] 0.7500000 1.0394737 24
## [221] 0.7105263 0.9847645 27
## [222] 0.9767442 1.3537332 42
## [223] 0.8076923 1.3673077 42
## [224] 0.7567568 1.6606607 28
## [225] 0.7027027 1.1895753 26
## [226] 0.7567568 1.0488383 28
## [227] 0.7804878 1.0817287 32
## [228] 0.7222222 1.2226190 26
## [229] 0.8055556 1.1164717 29
## [230] 0.8918919 1.5098456 33
## [231] 0.9729730 1.3485064 36
## [232] 0.8000000 1.3542857 24
## [233] 0.9000000 1.2473684 27
## [234] 0.7894737 1.0941828 30
## [235] 0.7714286 1.0691729 27
## [236] 0.7659574 1.0615901 36
## [237] 0.7619048 1.0559733 32
## [238] 0.8108108 1.1237553 30
## [239] 0.7272727 1.2311688 24
## [240] 0.7575758 1.0499734 25
## [241] 0.9803922 1.3587891 50
## [242] 0.8474576 1.4346247 50
## [243] 0.7575758 1.0499734 25
## [244] 0.9393939 1.5902597 31
## [245] 0.7045455 1.7763540 31
## [246] 0.9696970 1.3439660 32
## [247] 0.7812500 1.3225446 25
## [248] 0.8125000 1.1260965 26
## [249] 0.7500000 1.0394737 30
## [250] 0.7222222 1.0009747 26
## [251] 0.8285714 1.1483709 29
## [252] 0.7368421 1.0212373 28
## [253] 0.7837838 1.0862968 29
## [254] 0.9545455 1.3229665 42
## [255] 0.7500000 1.2696429 42
## [256] 0.7777778 1.0779727 28
## [257] 0.7631579 1.0577101 29
## [258] 0.7073171 1.5521680 29
## [259] 0.8780488 1.4864111 36
## [260] 0.9512195 1.3183569 39
## [261] 0.7619048 1.0559733 32
## [262] 0.7619048 1.0559733 32
## [263] 0.7105263 1.2028195 27
## [264] 0.8421053 1.1671283 32
## [265] 0.7173913 0.9942792 33
## [266] 0.7678571 1.0642231 43
## [267] 0.7959184 1.1031149 39
## [268] 0.7560976 1.0479247 31
## [269] 0.9655172 1.3381730 56
## [270] 0.8358209 1.4149254 56
## [271] 0.8709677 1.4744240 27
## [272] 0.9354839 1.2965478 29
## [273] 0.7619048 1.0559733 32
## [274] 0.7142857 0.9899749 30
## [275] 0.7111111 0.9855750 32
## [276] 0.9574468 1.3269877 45
## [277] 0.8035714 1.3603316 45
## [278] 0.8043478 1.1147979 37
## [279] 0.9000000 1.5235714 36
## [280] 0.9750000 1.3513158 39
## [281] 0.7500000 1.2696429 30
## [282] 0.9000000 1.2473684 36
## [283] 0.8604651 1.1925745 37
## [284] 0.7750000 1.0741228 31
## [285] 0.8181818 1.1339713 45
## [286] 0.8260870 1.1449275 38
## [287] 0.7111111 1.2038095 32
## [288] 0.8888889 1.2319688 40
## [289] 0.7500000 1.2696429 33
## [290] 0.8863636 1.2284689 39
## [291] 0.7111111 1.2038095 32
## [292] 0.7111111 0.9855750 32
## [293] 0.9687500 1.3426535 62
## [294] 0.8051948 1.3630798 62
## [295] 0.7346939 1.8137755 36
## [296] 0.8775510 1.4855685 43
## [297] 0.7049180 1.7772933 43
## [298] 0.9795918 1.3576799 48
## [299] 0.7735849 1.3095687 41
## [300] 0.8679245 1.2029129 46
## [301] 0.7317073 1.2386760 30
## [302] 0.8048780 1.1155327 33
## [303] 0.7179487 0.9950517 28
## [304] 0.7291667 1.0105994 35
## [305] 0.7551020 1.0465449 37
## [306] 0.7021277 1.1886018 33
## [307] 0.7872340 1.0910788 37
## [308] 0.8000000 1.1087719 40
## [309] 0.9836066 1.3632442 60
## [310] 0.8333333 1.4107143 60
## [311] 0.8909091 1.5081818 49
## [312] 0.7313433 1.8439187 49
## [313] 0.9636364 1.3355662 53
## [314] 0.9200000 1.5574286 46
## [315] 0.7540984 1.9012905 46
## [316] 0.9800000 1.3582456 49
## [317] 0.8571429 1.4510204 36
## [318] 1.0000000 1.3859649 42
## [319] 0.8604651 1.4566445 37
## [320] 0.9767442 1.3537332 42
## [321] 0.8333333 1.4107143 40
## [322] 0.9583333 1.3282164 46
## [323] 0.9069767 1.5353821 39
## [324] 0.9767442 1.3537332 42
## [325] 0.8444444 1.4295238 38
## [326] 0.9111111 1.2627680 41
## [327] 0.9512195 1.6102787 39
## [328] 0.9268293 1.2845528 38
## [329] 0.9756098 1.3521609 80
## [330] 0.8888889 1.5047619 80
## [331] 0.8181818 1.1339713 36
## [332] 0.8222222 1.1395712 37
## [333] 0.8085106 1.1205674 38
## [334] 0.8409091 1.1654705 37
## [335] 0.7659574 1.2966565 36
## [336] 0.8297872 1.1500560 39
## [337] 0.7500000 1.2696429 36
## [338] 0.8333333 1.1549708 40
## [339] 0.9701493 1.3445928 65
## [340] 0.8333333 1.4107143 65
## [341] 0.7142857 1.6277473 30
## [342] 0.7857143 1.0889724 33
## [343] 0.7818182 1.0835726 43
## [344] 0.8260870 1.1449275 38
## [345] 0.7924528 1.0983118 42
## [346] 0.8292683 1.1493368 34
## [347] 0.7058824 0.9783282 36
## [348] 0.9672131 1.3405234 59
## [349] 0.7972973 1.3497104 59
## [350] 0.7692308 1.0661269 40
## [351] 0.7804878 1.0817287 32
## [352] 0.7045455 1.1926948 31
## [353] 0.8409091 1.1654705 37
## [354] 0.7959184 1.1031149 39
## [355] 0.9830508 1.3624740 58
## [356] 0.7837838 1.3268340 58
## [357] 0.7800000 1.0810526 39
## [358] 0.8297872 1.1500560 39
## [359] 0.7800000 1.0810526 39
## [360] 0.9672131 1.3405234 59
## [361] 0.7468354 1.2642857 59
## [362] 0.8000000 1.1087719 40
## [363] 0.7916667 1.0972222 38
## [364] 0.7000000 0.9701754 35
## [365] 0.9701493 1.3445928 65
## [366] 0.7738095 1.3099490 65
## [367] 0.7719298 1.0698677 44
## [368] 0.9848485 1.3649654 65
## [369] 0.8227848 1.3928571 65
## [370] 0.9571429 1.3265664 67
## [371] 0.7790698 1.3188538 67
## [372] 0.9571429 1.3265664 67
## [373] 0.8701299 1.4730056 67
## [374] 1.0000000 1.3859649 31
## [375] 0.9687500 1.6399554 31
## [376] 1.0000000 2.5212766 31
## [377] 1.0000000 1.3859649 26
## [378] 0.9629630 1.6301587 26
## [379] 1.0000000 1.3859649 24
## [380] 0.8888889 1.5047619 24
## [381] 1.0000000 1.3859649 25
## [382] 0.8333333 1.4107143 25
## [383] 0.9615385 1.3326586 25
## [384] 0.8333333 1.4107143 25
## [385] 1.0000000 1.3859649 24
## [386] 0.8571429 1.4510204 24
## [387] 0.8965517 1.2425892 26
## [388] 0.8965517 1.5177340 26
## [389] 0.9629630 1.3346329 26
## [390] 1.0000000 1.6928571 26
## [391] 1.0000000 1.3859649 24
## [392] 0.8571429 1.4510204 24
## [393] 0.9615385 1.3326586 25
## [394] 0.8620690 1.4593596 25
## [395] 0.9615385 1.3326586 25
## [396] 0.8333333 1.4107143 25
## [397] 0.9615385 1.3326586 25
## [398] 0.8064516 1.3652074 25
## [399] 1.0000000 1.3859649 26
## [400] 0.9285714 1.5719388 26
## [401] 0.9629630 1.3346329 26
## [402] 0.8125000 1.3754464 26
## [403] 1.0000000 1.3859649 26
## [404] 0.8965517 1.5177340 26
## [405] 1.0000000 1.3859649 33
## [406] 0.9166667 1.5517857 33
## [407] 1.0000000 1.3859649 24
## [408] 0.8888889 1.5047619 24
## [409] 1.0000000 1.3859649 26
## [410] 0.8666667 1.4671429 26
## [411] 1.0000000 1.3859649 27
## [412] 0.7500000 1.2696429 27
## [413] 1.0000000 1.3859649 27
## [414] 0.8437500 1.4283482 27
## [415] 1.0000000 1.3859649 24
## [416] 0.8000000 1.3542857 24
## [417] 1.0000000 1.3859649 24
## [418] 0.9600000 1.6251429 24
## [419] 0.9677419 1.3412564 30
## [420] 0.9375000 1.5870536 30
## [421] 0.7142857 1.8009119 30
## [422] 0.9600000 1.3305263 24
## [423] 0.9230769 1.5626374 24
## [424] 1.0000000 1.3859649 24
## [425] 0.8571429 1.4510204 24
## [426] 0.9615385 1.3326586 25
## [427] 0.8620690 1.4593596 25
## [428] 0.9600000 1.6251429 24
## [429] 0.8275862 2.0865737 24
## [430] 0.9600000 1.3305263 24
## [431] 0.7500000 1.8909574 24
## [432] 0.8620690 1.4593596 25
## [433] 0.9310345 1.2903811 27
## [434] 0.9722222 1.3474659 35
## [435] 0.8974359 1.5192308 35
## [436] 0.9655172 1.3381730 28
## [437] 0.8750000 1.4812500 28
## [438] 0.9285714 1.2869674 26
## [439] 0.8125000 1.3754464 26
## [440] 1.0000000 1.3859649 27
## [441] 0.8437500 1.4283482 27
## [442] 0.9310345 1.2903811 27
## [443] 0.8181818 1.3850649 27
## [444] 0.8275862 1.1470054 24
## [445] 0.9473684 1.3130194 36
## [446] 0.8372093 1.4172757 36
## [447] 0.9696970 1.3439660 32
## [448] 0.8205128 1.3890110 32
## [449] 0.9230769 1.2793522 24
## [450] 0.7741935 1.3105991 24
## [451] 0.9600000 1.3305263 24
## [452] 0.8275862 1.4009852 24
## [453] 0.9629630 1.3346329 26
## [454] 0.8965517 1.5177340 26
## [455] 0.9629630 1.3346329 26
## [456] 0.8125000 1.3754464 26
## [457] 0.9230769 1.2793522 24
## [458] 0.7500000 1.2696429 24
## [459] 0.9615385 1.3326586 25
## [460] 0.8620690 1.4593596 25
## [461] 0.9333333 1.2935673 28
## [462] 0.8750000 1.4812500 28
## [463] 0.9600000 1.3305263 24
## [464] 0.8571429 1.4510204 24
## [465] 1.0000000 1.3859649 29
## [466] 0.7837838 1.3268340 29
## [467] 1.0000000 1.3859649 25
## [468] 1.0000000 1.3859649 25
## [469] 0.9722222 1.3474659 35
## [470] 0.8974359 1.5192308 35
## [471] 0.9615385 1.3326586 25
## [472] 0.9666667 1.3397661 29
## [473] 0.8055556 1.3636905 29
## [474] 0.8275862 1.1470054 24
## [475] 1.0000000 1.3859649 28
## [476] 0.7567568 1.2810811 28
## [477] 0.9615385 1.3326586 25
## [478] 0.8064516 1.3652074 25
## [479] 0.8333333 1.1549708 25
## [480] 1.0000000 1.3859649 33
## [481] 0.7333333 1.2414286 33
## [482] 1.0000000 1.3859649 29
## [483] 0.7631579 1.2919173 29
## [484] 1.0000000 1.3859649 32
## [485] 0.8000000 1.3542857 32
## [486] 0.9696970 1.3439660 32
## [487] 0.8205128 1.3890110 32
## [488] 0.9375000 1.2993421 30
## [489] 0.9375000 1.5870536 30
## [490] 0.9166667 1.5517857 33
## [491] 0.7674419 1.8946221 33
## [492] 0.8048780 2.0293202 33
## [493] 1.0000000 1.3859649 36
## [494] 0.7500000 1.8515625 36
## [495] 0.7826087 1.9731730 36
## [496] 0.9230769 1.5626374 24
## [497] 0.8000000 2.0170213 24
## [498] 1.0000000 1.3859649 26
## [499] 0.7878788 1.9864603 26
## [500] 1.0000000 1.3859649 24
## [501] 0.9629630 1.3346329 26
## [502] 0.7027027 1.7717079 26
## [503] 0.8965517 1.5177340 26
## [504] 0.7878788 1.9864603 26
## [505] 0.9655172 1.3381730 28
## [506] 0.7567568 1.9079931 28
## [507] 1.0000000 1.3859649 43
## [508] 0.8958333 1.5165179 43
## [509] 0.7166667 1.8069149 43
## [510] 0.8888889 1.2319688 24
## [511] 0.8275862 1.4009852 24
## [512] 0.7272727 1.7954545 24
## [513] 0.8965517 1.2425892 26
## [514] 0.7027027 1.7347973 26
## [515] 0.7741935 1.3105991 24
## [516] 0.7272727 1.7954545 24
## [517] 0.8709677 1.2071307 27
## [518] 1.0000000 1.3859649 41
## [519] 0.8913043 1.5088509 41
## [520] 0.9666667 1.3397661 29
## [521] 0.8787879 1.4876623 29
## [522] 0.9655172 1.3381730 28
## [523] 0.8000000 1.3542857 28
## [524] 0.9655172 1.3381730 28
## [525] 0.7567568 1.2810811 28
## [526] 0.9696970 1.3439660 32
## [527] 0.8648649 1.4640927 32
## [528] 0.9696970 1.3439660 32
## [529] 0.8000000 1.3542857 32
## [530] 0.9642857 1.3364662 27
## [531] 0.9310345 1.5761084 27
## [532] 0.9333333 1.5800000 28
## [533] 0.9333333 2.3531915 28
## [534] 0.9666667 1.3397661 29
## [535] 0.8055556 2.0310284 29
## [536] 1.0000000 1.3859649 27
## [537] 0.7297297 1.8398505 27
## [538] 0.9615385 1.3326586 25
## [539] 0.9642857 1.3364662 27
## [540] 0.7297297 1.8398505 27
## [541] 0.9629630 1.6301587 26
## [542] 0.7222222 1.8209220 26
## [543] 1.0000000 1.3859649 27
## [544] 0.9259259 1.5674603 25
## [545] 0.9259259 1.2833008 25
## [546] 0.9591837 1.3293949 47
## [547] 0.8867925 1.5012129 47
## [548] 0.7230769 1.8230769 47
## [549] 1.0000000 1.3859649 24
## [550] 0.7272727 1.8336557 24
## [551] 0.8928571 1.5114796 25
## [552] 0.7352941 1.8538798 25
## [553] 0.9642857 1.3364662 27
## [554] 0.9615385 1.3326586 25
## [555] 0.8965517 1.5177340 26
## [556] 0.7647059 1.9280350 26
## [557] 1.0000000 1.3859649 29
## [558] 0.9782609 1.3558352 45
## [559] 0.9183673 1.5546647 45
## [560] 0.7627119 1.9230076 45
## [561] 1.0000000 1.3859649 24
## [562] 1.0000000 1.3859649 36
## [563] 0.8571429 1.4510204 36
## [564] 0.9615385 1.3326586 25
## [565] 0.9729730 1.3485064 36
## [566] 0.8571429 1.4510204 36
## [567] 0.9750000 1.3513158 39
## [568] 0.8478261 1.4352484 39
## [569] 0.9600000 1.3305263 24
## [570] 1.0000000 1.3859649 39
## [571] 0.9285714 1.5719388 39
## [572] 0.9473684 1.3130194 36
## [573] 0.8780488 1.4864111 36
## [574] 0.9743590 1.3504274 38
## [575] 1.0000000 1.6928571 38
## [576] 0.9666667 1.3397661 29
## [577] 0.8055556 1.3636905 29
## [578] 1.0000000 1.3859649 30
## [579] 0.8108108 1.3725869 30
## [580] 0.9666667 1.3397661 29
## [581] 0.7631579 1.2919173 29
## [582] 0.9642857 1.3364662 27
## [583] 0.7297297 1.2353282 27
## [584] 1.0000000 1.3859649 36
## [585] 0.9230769 1.5626374 36
## [586] 0.9444444 1.3089669 34
## [587] 0.8500000 1.4389286 34
## [588] 0.9615385 1.3326586 25
## [589] 0.9259259 1.5674603 25
## [590] 0.8333333 1.1549708 25
## [591] 0.7575758 1.7263986 25
## [592] 1.0000000 1.3859649 26
## [593] 0.7878788 1.3337662 26
## [594] 0.8064516 1.1177136 25
## [595] 0.8125000 1.1260965 26
## [596] 0.9411765 1.3044376 32
## [597] 0.7441860 1.2598007 32
## [598] 0.9354839 1.2965478 29
## [599] 0.7631579 1.2919173 29
## [600] 0.9705882 1.3452012 33
## [601] 0.7857143 1.3301020 33
## [602] 0.9285714 1.2869674 26
## [603] 0.7647059 1.2945378 26
## [604] 0.9696970 1.3439660 32
## [605] 0.8888889 1.5047619 32
## [606] 1.0000000 1.3859649 29
## [607] 0.7250000 1.2273214 29
## [608] 1.0000000 1.3859649 26
## [609] 0.8125000 1.3754464 26
## [610] 1.0000000 1.3859649 31
## [611] 0.8378378 1.4183398 31
## [612] 1.0000000 1.3859649 30
## [613] 0.7692308 1.3021978 30
## [614] 0.9642857 1.3364662 27
## [615] 0.9310345 1.5761084 27
## [616] 0.9354839 1.2965478 29
## [617] 0.7435897 1.2587912 29
## [618] 0.9677419 1.3412564 30
## [619] 0.7692308 1.3021978 30
## [620] 0.9375000 1.2993421 30
## [621] 0.7692308 1.3021978 30
## [622] 0.9696970 1.3439660 32
## [623] 0.8648649 1.4640927 32
## [624] 0.9687500 1.3426535 31
## [625] 0.7750000 1.3119643 31
## [626] 0.9375000 1.2993421 30
## [627] 0.7894737 1.3364662 30
## [628] 0.9666667 1.3397661 29
## [629] 0.8285714 1.4026531 29
## [630] 0.9743590 1.3504274 38
## [631] 0.8636364 1.4620130 38
## [632] 0.9696970 1.3439660 32
## [633] 0.8205128 1.3890110 32
## [634] 0.9411765 1.3044376 32
## [635] 0.8648649 1.4640927 32
## [636] 0.9600000 1.3305263 24
## [637] 0.8888889 1.5047619 24
## [638] 1.0000000 1.3859649 33
## [639] 0.9166667 1.5517857 33
## [640] 0.7674419 1.8946221 33
## [641] 0.8048780 2.0293202 33
## [642] 0.7021277 1.7155078 33
## [643] 1.0000000 1.3859649 24
## [644] 0.9230769 1.5626374 24
## [645] 0.8275862 2.0865737 24
## [646] 1.0000000 1.3859649 26
## [647] 0.9285714 1.5719388 26
## [648] 0.8125000 2.0485372 26
## [649] 1.0000000 1.3859649 24
## [650] 0.9230769 1.5626374 24
## [651] 0.7500000 1.8515625 24
## [652] 1.0000000 1.3859649 24
## [653] 0.8888889 1.5047619 24
## [654] 0.7500000 1.8515625 24
## [655] 0.7058824 1.7246816 24
## [656] 0.9642857 1.3364662 27
## [657] 0.9310345 1.5761084 27
## [658] 0.9310345 2.3473955 27
## [659] 1.0000000 1.3859649 26
## [660] 0.9629630 1.6301587 26
## [661] 0.7222222 1.8209220 26
## [662] 0.9600000 1.3305263 24
## [663] 0.8888889 1.5047619 24
## [664] 0.7500000 1.8909574 24
## [665] 1.0000000 1.3859649 26
## [666] 0.8965517 1.5177340 26
## [667] 0.7878788 1.9864603 26
## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 62
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[5767 item(s), 622 transaction(s)] done [0.00s].
## sorting and recoding items ... [103 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [364 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support confidence
## [1] {cnn} => {convolut} 0.1012862 0.8513514
## [2] {cnn} => {neural} 0.1045016 0.8783784
## [3] {cnn} => {network} 0.1125402 0.9459459
## [4] {outperform} => {network} 0.1012862 0.7590361
## [5] {general} => {network} 0.1045016 0.7738095
## [6] {number} => {network} 0.1028939 0.7901235
## [7] {input} => {network} 0.1093248 0.8500000
## [8] {layer} => {network} 0.1189711 0.8809524
## [9] {effici} => {network} 0.1061093 0.7415730
## [10] {test} => {network} 0.1157556 0.7578947
## [11] {complex} => {network} 0.1093248 0.7727273
## [12] {design} => {network} 0.1012862 0.7078652
## [13] {optim} => {network} 0.1189711 0.7708333
## [14] {function} => {network} 0.1221865 0.7755102
## [15] {requir} => {network} 0.1077170 0.7052632
## [16] {appli} => {network} 0.1254019 0.7500000
## [17] {includ} => {network} 0.1205788 0.7653061
## [18] {detect} => {network} 0.1205788 0.7009346
## [19] {larg} => {network} 0.1318328 0.7663551
## [20] {stateoftheart} => {network} 0.1286174 0.7476636
## [21] {analysi} => {network} 0.1318328 0.7387387
## [22] {inform} => {network} 0.1302251 0.7043478
## [23] {set} => {network} 0.1463023 0.7844828
## [24] {learn} => {network} 0.1350482 0.7000000
## [25] {evalu} => {network} 0.1463023 0.7520661
## [26] {time} => {network} 0.1559486 0.8151261
## [27] {signific} => {network} 0.1398714 0.7250000
## [28] {studi} => {network} 0.1655949 0.8240000
## [29] {compar} => {network} 0.1543408 0.7384615
## [30] {process} => {network} 0.1511254 0.7343750
## [31] {architectur} => {network} 0.1704180 0.7737226
## [32] {work} => {network} 0.1623794 0.7112676
## [33] {problem} => {network} 0.1575563 0.7000000
## [34] {classif} => {network} 0.1736334 0.7500000
## [35] {predict} => {network} 0.1768489 0.7482993
## [36] {high} => {network} 0.1655949 0.7006803
## [37] {algorithm} => {network} 0.1816720 0.7434211
## [38] {accuraci} => {network} 0.1864952 0.7532468
## [39] {achiev} => {network} 0.1881029 0.7597403
## [40] {dataset} => {network} 0.1945338 0.7469136
## [41] {demonstr} => {network} 0.1816720 0.7243590
## [42] {task} => {network} 0.1929260 0.7407407
## [43] {improv} => {network} 0.1848875 0.7324841
## [44] {featur} => {network} 0.1961415 0.7261905
## [45] {comput} => {network} 0.2041801 0.7134831
## [46] {convolut} => {neural} 0.2765273 0.8686869
## [47] {convolut} => {network} 0.3038585 0.9545455
## [48] {imag} => {network} 0.2491961 0.7345972
## [49] {base} => {network} 0.2524116 0.7370892
## [50] {show} => {network} 0.2733119 0.7623318
## [51] {paper} => {network} 0.3054662 0.7089552
## [52] {perform} => {network} 0.3231511 0.7416974
## [53] {propos} => {network} 0.3376206 0.7664234
## [54] {method} => {network} 0.3135048 0.7116788
## [55] {result} => {network} 0.3135048 0.7142857
## [56] {data} => {network} 0.3295820 0.7243816
## [57] {train} => {network} 0.3665595 0.7755102
## [58] {model} => {network} 0.3569132 0.7138264
## [59] {neural} => {network} 0.5530547 0.9800570
## [60] {network} => {neural} 0.5530547 0.7818182
## [61] {neural,cnn} => {network} 0.1012862 0.9692308
## [62] {network,cnn} => {neural} 0.1012862 0.9000000
## [63] {appli,neural} => {network} 0.1012862 0.9843750
## [64] {appli,network} => {neural} 0.1012862 0.8076923
## [65] {neural,larg} => {network} 0.1012862 1.0000000
## [66] {network,larg} => {neural} 0.1012862 0.7682927
## [67] {neural,effect} => {network} 0.1012862 0.9843750
## [68] {network,effect} => {neural} 0.1012862 0.8076923
## [69] {neural,inform} => {network} 0.1028939 0.9552239
## [70] {network,inform} => {neural} 0.1028939 0.7901235
## [71] {neural,set} => {network} 0.1077170 0.9710145
## [72] {network,set} => {neural} 0.1077170 0.7362637
## [73] {neural,learn} => {network} 0.1012862 0.9843750
## [74] {network,learn} => {neural} 0.1012862 0.7500000
## [75] {evalu,neural} => {network} 0.1109325 1.0000000
## [76] {evalu,network} => {neural} 0.1109325 0.7582418
## [77] {neural,time} => {network} 0.1141479 0.9726027
## [78] {network,time} => {neural} 0.1141479 0.7319588
## [79] {neural,signific} => {network} 0.1141479 0.9726027
## [80] {network,signific} => {neural} 0.1141479 0.8160920
## [81] {neural,studi} => {network} 0.1254019 0.9873418
## [82] {network,studi} => {neural} 0.1254019 0.7572816
## [83] {develop,neural} => {network} 0.1125402 0.9859155
## [84] {develop,network} => {neural} 0.1125402 0.8139535
## [85] {neural,challeng} => {network} 0.1028939 0.9846154
## [86] {network,challeng} => {neural} 0.1028939 0.7441860
## [87] {neural,techniqu} => {network} 0.1061093 0.9850746
## [88] {network,techniqu} => {neural} 0.1061093 0.7674419
## [89] {experi,neural} => {network} 0.1077170 0.9852941
## [90] {experi,network} => {neural} 0.1077170 0.7613636
## [91] {neural,recent} => {network} 0.1012862 0.9692308
## [92] {network,recent} => {neural} 0.1012862 0.7159091
## [93] {neural,compar} => {network} 0.1157556 0.9729730
## [94] {network,compar} => {neural} 0.1157556 0.7500000
## [95] {neural,process} => {network} 0.1189711 0.9736842
## [96] {network,process} => {neural} 0.1189711 0.7872340
## [97] {neural,architectur} => {network} 0.1221865 0.9620253
## [98] {network,architectur} => {neural} 0.1221865 0.7169811
## [99] {neural,work} => {network} 0.1302251 0.9759036
## [100] {network,work} => {neural} 0.1302251 0.8019802
## [101] {machin,neural} => {network} 0.1189711 1.0000000
## [102] {machin,network} => {neural} 0.1189711 0.8131868
## [103] {neural,problem} => {network} 0.1173633 0.9733333
## [104] {network,problem} => {neural} 0.1173633 0.7448980
## [105] {neural,classif} => {network} 0.1398714 0.9560440
## [106] {network,classif} => {neural} 0.1398714 0.8055556
## [107] {applic,neural} => {network} 0.1189711 1.0000000
## [108] {applic,network} => {neural} 0.1189711 0.7872340
## [109] {model,predict} => {network} 0.1109325 0.7340426
## [110] {neural,predict} => {network} 0.1366559 1.0000000
## [111] {network,predict} => {neural} 0.1366559 0.7727273
## [112] {high,neural} => {network} 0.1302251 0.9878049
## [113] {high,network} => {neural} 0.1302251 0.7864078
## [114] {neural,algorithm} => {network} 0.1366559 0.9550562
## [115] {network,algorithm} => {neural} 0.1366559 0.7522124
## [116] {accuraci,train} => {network} 0.1061093 0.7674419
## [117] {model,accuraci} => {network} 0.1061093 0.7951807
## [118] {neural,accuraci} => {network} 0.1430868 0.9888889
## [119] {network,accuraci} => {neural} 0.1430868 0.7672414
## [120] {achiev,perform} => {network} 0.1141479 0.7717391
## [121] {propos,achiev} => {network} 0.1012862 0.7777778
## [122] {achiev,model} => {network} 0.1012862 0.7500000
## [123] {achiev,neural} => {network} 0.1446945 0.9677419
## [124] {achiev,network} => {neural} 0.1446945 0.7692308
## [125] {dataset,convolut} => {neural} 0.1012862 0.8873239
## [126] {dataset,convolut} => {network} 0.1093248 0.9577465
## [127] {dataset,paper} => {network} 0.1125402 0.8235294
## [128] {dataset,propos} => {network} 0.1077170 0.8072289
## [129] {dataset,method} => {network} 0.1012862 0.7974684
## [130] {dataset,train} => {network} 0.1109325 0.7752809
## [131] {dataset,model} => {network} 0.1141479 0.7717391
## [132] {dataset,neural} => {network} 0.1511254 0.9791667
## [133] {dataset,network} => {neural} 0.1511254 0.7768595
## [134] {demonstr,neural} => {network} 0.1382637 0.9772727
## [135] {demonstr,network} => {neural} 0.1382637 0.7610619
## [136] {paper,task} => {network} 0.1028939 0.7356322
## [137] {model,task} => {network} 0.1061093 0.7173913
## [138] {neural,task} => {network} 0.1495177 0.9893617
## [139] {network,task} => {neural} 0.1495177 0.7750000
## [140] {improv,perform} => {network} 0.1109325 0.8214286
## [141] {propos,improv} => {network} 0.1012862 0.7875000
## [142] {train,improv} => {network} 0.1093248 0.8292683
## [143] {model,improv} => {network} 0.1061093 0.7764706
## [144] {neural,improv} => {network} 0.1302251 0.9529412
## [145] {network,improv} => {neural} 0.1302251 0.7043478
## [146] {neural,system} => {network} 0.1398714 0.9666667
## [147] {network,system} => {neural} 0.1398714 0.8285714
## [148] {convolut,featur} => {network} 0.1061093 0.9565217
## [149] {featur,perform} => {network} 0.1061093 0.7333333
## [150] {method,featur} => {network} 0.1061093 0.7674419
## [151] {data,featur} => {network} 0.1061093 0.7586207
## [152] {featur,model} => {network} 0.1028939 0.7441860
## [153] {featur,neural} => {network} 0.1575563 1.0000000
## [154] {featur,network} => {neural} 0.1575563 0.8032787
## [155] {present,convolut} => {network} 0.1028939 0.9552239
## [156] {present,neural} => {network} 0.1607717 0.9803922
## [157] {present,network} => {neural} 0.1607717 0.8130081
## [158] {propos,comput} => {network} 0.1045016 0.7738095
## [159] {train,comput} => {network} 0.1045016 0.7926829
## [160] {model,comput} => {network} 0.1173633 0.7765957
## [161] {neural,comput} => {network} 0.1527331 0.9895833
## [162] {network,comput} => {neural} 0.1527331 0.7480315
## [163] {convolut,imag} => {neural} 0.1446945 0.8910891
## [164] {convolut,imag} => {network} 0.1527331 0.9405941
## [165] {base,convolut} => {neural} 0.1189711 0.8705882
## [166] {base,convolut} => {network} 0.1318328 0.9647059
## [167] {convolut,show} => {network} 0.1077170 0.9436620
## [168] {convolut,approach} => {network} 0.1077170 0.9571429
## [169] {convolut,paper} => {neural} 0.1221865 0.8837209
## [170] {convolut,paper} => {network} 0.1334405 0.9651163
## [171] {convolut,perform} => {neural} 0.1398714 0.8877551
## [172] {convolut,perform} => {network} 0.1495177 0.9489796
## [173] {propos,convolut} => {neural} 0.1237942 0.8651685
## [174] {propos,convolut} => {network} 0.1398714 0.9775281
## [175] {method,convolut} => {neural} 0.1318328 0.8913043
## [176] {method,convolut} => {network} 0.1446945 0.9782609
## [177] {result,convolut} => {neural} 0.1366559 0.8947368
## [178] {result,convolut} => {network} 0.1495177 0.9789474
## [179] {data,convolut} => {neural} 0.1318328 0.8723404
## [180] {data,convolut} => {network} 0.1446945 0.9574468
## [181] {convolut,train} => {neural} 0.1479100 0.8761905
## [182] {convolut,train} => {network} 0.1591640 0.9428571
## [183] {convolut,model} => {neural} 0.1430868 0.8476190
## [184] {convolut,model} => {network} 0.1575563 0.9333333
## [185] {convolut,neural} => {network} 0.2733119 0.9883721
## [186] {convolut,network} => {neural} 0.2733119 0.8994709
## [187] {paper,imag} => {network} 0.1077170 0.7282609
## [188] {imag,perform} => {network} 0.1254019 0.7878788
## [189] {propos,imag} => {network} 0.1318328 0.8282828
## [190] {method,imag} => {network} 0.1302251 0.7168142
## [191] {result,imag} => {network} 0.1334405 0.7410714
## [192] {data,imag} => {network} 0.1061093 0.7333333
## [193] {train,imag} => {network} 0.1430868 0.7478992
## [194] {model,imag} => {network} 0.1173633 0.7604167
## [195] {neural,imag} => {network} 0.2025723 0.9767442
## [196] {network,imag} => {neural} 0.2025723 0.8129032
## [197] {base,show} => {network} 0.1157556 0.8181818
## [198] {base,approach} => {network} 0.1205788 0.7812500
## [199] {base,paper} => {network} 0.1173633 0.7373737
## [200] {base,perform} => {network} 0.1254019 0.7722772
## [201] {propos,base} => {network} 0.1254019 0.7358491
## [202] {method,base} => {network} 0.1254019 0.7090909
## [203] {result,base} => {network} 0.1221865 0.7169811
## [204] {data,base} => {network} 0.1221865 0.7755102
## [205] {base,train} => {network} 0.1270096 0.7900000
## [206] {base,model} => {network} 0.1398714 0.7565217
## [207] {base,neural} => {network} 0.2057878 0.9846154
## [208] {base,network} => {neural} 0.2057878 0.8152866
## [209] {paper,show} => {network} 0.1286174 0.7407407
## [210] {show,perform} => {network} 0.1173633 0.7525773
## [211] {propos,show} => {network} 0.1350482 0.7706422
## [212] {method,show} => {network} 0.1286174 0.7619048
## [213] {result,show} => {network} 0.1446945 0.7563025
## [214] {data,show} => {network} 0.1334405 0.7685185
## [215] {train,show} => {network} 0.1479100 0.7796610
## [216] {model,show} => {network} 0.1382637 0.7747748
## [217] {neural,show} => {network} 0.2090032 0.9629630
## [218] {network,show} => {neural} 0.2090032 0.7647059
## [219] {approach,perform} => {network} 0.1254019 0.7155963
## [220] {propos,approach} => {network} 0.1382637 0.7543860
## [221] {approach,train} => {network} 0.1318328 0.7192982
## [222] {model,approach} => {network} 0.1237942 0.7129630
## [223] {neural,approach} => {network} 0.1913183 0.9754098
## [224] {network,approach} => {neural} 0.1913183 0.7727273
## [225] {paper,perform} => {network} 0.1414791 0.7457627
## [226] {propos,paper} => {network} 0.1832797 0.7450980
## [227] {result,paper} => {network} 0.1398714 0.7016129
## [228] {data,paper} => {network} 0.1382637 0.7166667
## [229] {paper,train} => {network} 0.1286174 0.7476636
## [230] {model,paper} => {network} 0.1543408 0.7164179
## [231] {neural,paper} => {network} 0.2250804 0.9722222
## [232] {network,paper} => {neural} 0.2250804 0.7368421
## [233] {propos,perform} => {network} 0.1575563 0.7903226
## [234] {method,perform} => {network} 0.1463023 0.7583333
## [235] {result,perform} => {network} 0.1479100 0.7731092
## [236] {data,perform} => {network} 0.1511254 0.7520000
## [237] {train,perform} => {network} 0.1704180 0.7571429
## [238] {model,perform} => {network} 0.1655949 0.7463768
## [239] {neural,perform} => {network} 0.2572347 0.9876543
## [240] {network,perform} => {neural} 0.2572347 0.7960199
## [241] {method,propos} => {network} 0.1655949 0.7103448
## [242] {propos,result} => {network} 0.1688103 0.7553957
## [243] {data,propos} => {network} 0.1655949 0.7744361
## [244] {propos,train} => {network} 0.1897106 0.8489209
## [245] {propos,model} => {network} 0.1832797 0.8028169
## [246] {propos,neural} => {network} 0.2556270 0.9814815
## [247] {propos,network} => {neural} 0.2556270 0.7571429
## [248] {data,method} => {network} 0.1639871 0.7669173
## [249] {method,train} => {network} 0.1816720 0.8188406
## [250] {method,model} => {network} 0.1672026 0.7761194
## [251] {method,neural} => {network} 0.2379421 0.9801325
## [252] {method,network} => {neural} 0.2379421 0.7589744
## [253] {result,train} => {network} 0.1720257 0.8230769
## [254] {result,model} => {network} 0.1720257 0.7642857
## [255] {result,neural} => {network} 0.2443730 0.9743590
## [256] {result,network} => {neural} 0.2443730 0.7794872
## [257] {data,train} => {network} 0.2073955 0.7771084
## [258] {data,model} => {network} 0.1864952 0.7250000
## [259] {data,neural} => {network} 0.2443730 0.9743590
## [260] {data,network} => {neural} 0.2443730 0.7414634
## [261] {model,train} => {network} 0.2073955 0.7678571
## [262] {neural,train} => {network} 0.2942122 0.9945652
## [263] {network,train} => {neural} 0.2942122 0.8026316
## [264] {model,neural} => {network} 0.2781350 0.9829545
## [265] {model,network} => {neural} 0.2781350 0.7792793
## [266] {dataset,convolut,neural} => {network} 0.1012862 1.0000000
## [267] {dataset,convolut,network} => {neural} 0.1012862 0.9264706
## [268] {convolut,neural,imag} => {network} 0.1414791 0.9777778
## [269] {convolut,network,imag} => {neural} 0.1414791 0.9263158
## [270] {base,convolut,neural} => {network} 0.1189711 1.0000000
## [271] {base,convolut,network} => {neural} 0.1189711 0.9024390
## [272] {convolut,neural,paper} => {network} 0.1205788 0.9868421
## [273] {convolut,network,paper} => {neural} 0.1205788 0.9036145
## [274] {convolut,neural,perform} => {network} 0.1382637 0.9885057
## [275] {convolut,network,perform} => {neural} 0.1382637 0.9247312
## [276] {propos,convolut,neural} => {network} 0.1237942 1.0000000
## [277] {propos,convolut,network} => {neural} 0.1237942 0.8850575
## [278] {method,convolut,neural} => {network} 0.1302251 0.9878049
## [279] {method,convolut,network} => {neural} 0.1302251 0.9000000
## [280] {result,convolut,neural} => {network} 0.1350482 0.9882353
## [281] {result,convolut,network} => {neural} 0.1350482 0.9032258
## [282] {data,convolut,neural} => {network} 0.1286174 0.9756098
## [283] {data,convolut,network} => {neural} 0.1286174 0.8888889
## [284] {convolut,neural,train} => {network} 0.1463023 0.9891304
## [285] {convolut,network,train} => {neural} 0.1463023 0.9191919
## [286] {convolut,model,neural} => {network} 0.1414791 0.9887640
## [287] {convolut,model,network} => {neural} 0.1414791 0.8979592
## [288] {neural,imag,perform} => {network} 0.1077170 0.9852941
## [289] {network,imag,perform} => {neural} 0.1077170 0.8589744
## [290] {result,neural,imag} => {network} 0.1077170 0.9852941
## [291] {result,network,imag} => {neural} 0.1077170 0.8072289
## [292] {neural,train,imag} => {network} 0.1221865 0.9870130
## [293] {network,train,imag} => {neural} 0.1221865 0.8539326
## [294] {base,neural,perform} => {network} 0.1061093 1.0000000
## [295] {base,network,perform} => {neural} 0.1061093 0.8461538
## [296] {base,neural,train} => {network} 0.1109325 1.0000000
## [297] {base,network,train} => {neural} 0.1109325 0.8734177
## [298] {base,model,neural} => {network} 0.1077170 0.9710145
## [299] {base,model,network} => {neural} 0.1077170 0.7701149
## [300] {propos,neural,show} => {network} 0.1012862 0.9692308
## [301] {propos,network,show} => {neural} 0.1012862 0.7500000
## [302] {result,neural,show} => {network} 0.1045016 0.9420290
## [303] {result,network,show} => {neural} 0.1045016 0.7222222
## [304] {neural,train,show} => {network} 0.1173633 0.9864865
## [305] {network,train,show} => {neural} 0.1173633 0.7934783
## [306] {propos,neural,approach} => {network} 0.1012862 1.0000000
## [307] {propos,network,approach} => {neural} 0.1012862 0.7325581
## [308] {neural,approach,train} => {network} 0.1028939 0.9846154
## [309] {network,approach,train} => {neural} 0.1028939 0.7804878
## [310] {neural,paper,perform} => {network} 0.1093248 1.0000000
## [311] {network,paper,perform} => {neural} 0.1093248 0.7727273
## [312] {propos,neural,paper} => {network} 0.1334405 0.9764706
## [313] {propos,network,paper} => {neural} 0.1334405 0.7280702
## [314] {neural,paper,train} => {network} 0.1012862 0.9843750
## [315] {network,paper,train} => {neural} 0.1012862 0.7875000
## [316] {model,neural,paper} => {network} 0.1125402 0.9722222
## [317] {model,network,paper} => {neural} 0.1125402 0.7291667
## [318] {propos,neural,perform} => {network} 0.1189711 1.0000000
## [319] {propos,network,perform} => {neural} 0.1189711 0.7551020
## [320] {method,neural,perform} => {network} 0.1109325 1.0000000
## [321] {method,network,perform} => {neural} 0.1109325 0.7582418
## [322] {result,neural,perform} => {network} 0.1157556 0.9729730
## [323] {result,network,perform} => {neural} 0.1157556 0.7826087
## [324] {data,neural,perform} => {network} 0.1221865 0.9870130
## [325] {data,network,perform} => {neural} 0.1221865 0.8085106
## [326] {neural,train,perform} => {network} 0.1382637 1.0000000
## [327] {network,train,perform} => {neural} 0.1382637 0.8113208
## [328] {model,neural,perform} => {network} 0.1318328 1.0000000
## [329] {model,network,perform} => {neural} 0.1318328 0.7961165
## [330] {method,propos,neural} => {network} 0.1205788 0.9740260
## [331] {method,propos,network} => {neural} 0.1205788 0.7281553
## [332] {propos,result,model} => {network} 0.1061093 0.8684211
## [333] {propos,result,neural} => {network} 0.1237942 0.9746835
## [334] {propos,result,network} => {neural} 0.1237942 0.7333333
## [335] {data,propos,train} => {network} 0.1093248 0.8395062
## [336] {data,propos,neural} => {network} 0.1189711 0.9736842
## [337] {data,propos,network} => {neural} 0.1189711 0.7184466
## [338] {propos,model,train} => {network} 0.1189711 0.8915663
## [339] {propos,neural,train} => {network} 0.1430868 1.0000000
## [340] {propos,network,train} => {neural} 0.1430868 0.7542373
## [341] {propos,model,neural} => {network} 0.1366559 0.9770115
## [342] {propos,model,network} => {neural} 0.1366559 0.7456140
## [343] {method,result,neural} => {network} 0.1028939 0.9846154
## [344] {method,result,network} => {neural} 0.1028939 0.7356322
## [345] {data,method,train} => {network} 0.1141479 0.8452381
## [346] {data,method,neural} => {network} 0.1254019 0.9750000
## [347] {data,method,network} => {neural} 0.1254019 0.7647059
## [348] {method,model,train} => {network} 0.1061093 0.8800000
## [349] {method,neural,train} => {network} 0.1366559 0.9883721
## [350] {method,network,train} => {neural} 0.1366559 0.7522124
## [351] {method,model,neural} => {network} 0.1254019 0.9750000
## [352] {method,model,network} => {neural} 0.1254019 0.7500000
## [353] {result,model,train} => {network} 0.1093248 0.8500000
## [354] {result,neural,train} => {network} 0.1430868 1.0000000
## [355] {result,network,train} => {neural} 0.1430868 0.8317757
## [356] {result,model,neural} => {network} 0.1270096 0.9753086
## [357] {result,model,network} => {neural} 0.1270096 0.7383178
## [358] {data,model,train} => {network} 0.1221865 0.7524752
## [359] {data,neural,train} => {network} 0.1623794 0.9901961
## [360] {data,network,train} => {neural} 0.1623794 0.7829457
## [361] {data,model,neural} => {network} 0.1430868 0.9780220
## [362] {data,model,network} => {neural} 0.1430868 0.7672414
## [363] {model,neural,train} => {network} 0.1639871 0.9902913
## [364] {model,network,train} => {neural} 0.1639871 0.7906977
## lift count
## [1] 2.6744472 63
## [2] 1.5565566 65
## [3] 1.3372236 70
## [4] 1.0730011 63
## [5] 1.0938853 65
## [6] 1.1169473 64
## [7] 1.2015909 68
## [8] 1.2453463 74
## [9] 1.0483146 66
## [10] 1.0713876 72
## [11] 1.0923554 68
## [12] 1.0006639 63
## [13] 1.0896780 74
## [14] 1.0962894 76
## [15] 0.9969856 67
## [16] 1.0602273 78
## [17] 1.0818646 75
## [18] 0.9908666 75
## [19] 1.0833475 82
## [20] 1.0569244 80
## [21] 1.0443079 82
## [22] 0.9956917 81
## [23] 1.1089734 91
## [24] 0.9895455 84
## [25] 1.0631480 91
## [26] 1.1522918 97
## [27] 1.0248864 87
## [28] 1.1648364 103
## [29] 1.0439161 96
## [30] 1.0381392 94
## [31] 1.0937624 106
## [32] 1.0054738 101
## [33] 0.9895455 98
## [34] 1.0602273 108
## [35] 1.0578231 110
## [36] 0.9905071 103
## [37] 1.0509270 113
## [38] 1.0648170 116
## [39] 1.0739965 117
## [40] 1.0558642 121
## [41] 1.0239802 113
## [42] 1.0471380 120
## [43] 1.0354661 115
## [44] 1.0265693 122
## [45] 1.0086057 127
## [46] 1.5393824 172
## [47] 1.3493802 189
## [48] 1.0384533 155
## [49] 1.0419761 157
## [50] 1.0776600 170
## [51] 1.0022049 190
## [52] 1.0484904 201
## [53] 1.0834439 210
## [54] 1.0060551 195
## [55] 1.0097403 195
## [56] 1.0240122 205
## [57] 1.0962894 228
## [58] 1.0090909 222
## [59] 1.3854442 344
## [60] 1.3854442 344
## [61] 1.3701399 63
## [62] 1.5948718 63
## [63] 1.3915483 63
## [64] 1.4312952 63
## [65] 1.4136364 63
## [66] 1.3614759 63
## [67] 1.3915483 63
## [68] 1.4312952 63
## [69] 1.3503392 64
## [70] 1.4001618 64
## [71] 1.3726614 67
## [72] 1.3047181 67
## [73] 1.3915483 63
## [74] 1.3290598 63
## [75] 1.4136364 69
## [76] 1.3436649 69
## [77] 1.3749066 71
## [78] 1.2970893 71
## [79] 1.3749066 71
## [80] 1.4461800 71
## [81] 1.3957422 78
## [82] 1.3419633 78
## [83] 1.3937260 70
## [84] 1.4423905 70
## [85] 1.3918881 64
## [86] 1.3187570 64
## [87] 1.3925373 66
## [88] 1.3599682 66
## [89] 1.3928476 67
## [90] 1.3491971 67
## [91] 1.3701399 63
## [92] 1.2686480 63
## [93] 1.3754300 72
## [94] 1.3290598 72
## [95] 1.3764354 74
## [96] 1.3950415 74
## [97] 1.3599540 76
## [98] 1.2705478 76
## [99] 1.3795728 81
## [100] 1.4211729 81
## [101] 1.4136364 74
## [102] 1.4410319 74
## [103] 1.3759394 73
## [104] 1.3200186 73
## [105] 1.3514985 87
## [106] 1.4275087 87
## [107] 1.4136364 74
## [108] 1.3950415 74
## [109] 1.0376692 69
## [110] 1.4136364 85
## [111] 1.3693344 85
## [112] 1.3963969 81
## [113] 1.3935773 81
## [114] 1.3501021 85
## [115] 1.3329804 85
## [116] 1.0848837 66
## [117] 1.1240964 66
## [118] 1.3979293 89
## [119] 1.3596129 89
## [120] 1.0909585 71
## [121] 1.0994949 63
## [122] 1.0602273 63
## [123] 1.3680352 90
## [124] 1.3631383 90
## [125] 1.5724088 63
## [126] 1.3539052 68
## [127] 1.1641711 70
## [128] 1.1411281 67
## [129] 1.1273303 63
## [130] 1.0959653 69
## [131] 1.0909585 71
## [132] 1.3841856 94
## [133] 1.3766570 94
## [134] 1.3815083 86
## [135] 1.3486625 86
## [136] 1.0399164 64
## [137] 1.0141304 66
## [138] 1.3985977 93
## [139] 1.3733618 93
## [140] 1.1612013 69
## [141] 1.1132386 63
## [142] 1.1722838 68
## [143] 1.0976471 66
## [144] 1.3471123 81
## [145] 1.2481605 81
## [146] 1.3665152 87
## [147] 1.4682947 87
## [148] 1.3521739 66
## [149] 1.0366667 66
## [150] 1.0848837 66
## [151] 1.0724138 66
## [152] 1.0520085 64
## [153] 1.4136364 98
## [154] 1.4234739 98
## [155] 1.3503392 64
## [156] 1.3859180 100
## [157] 1.4407153 100
## [158] 1.0938853 65
## [159] 1.1205654 65
## [160] 1.0978240 73
## [161] 1.3989110 95
## [162] 1.3255715 95
## [163] 1.5790810 90
## [164] 1.3296580 95
## [165] 1.5427518 74
## [166] 1.3637433 82
## [167] 1.3339949 67
## [168] 1.3530519 67
## [169] 1.5660240 76
## [170] 1.3643235 83
## [171] 1.5731729 87
## [172] 1.3415121 93
## [173] 1.5331477 77
## [174] 1.3818693 87
## [175] 1.5794624 82
## [176] 1.3829051 90
## [177] 1.5855451 85
## [178] 1.3838756 93
## [179] 1.5458568 82
## [180] 1.3534816 90
## [181] 1.5526794 92
## [182] 1.3328571 99
## [183] 1.5020486 89
## [184] 1.3193939 98
## [185] 1.3971987 170
## [186] 1.5939342 170
## [187] 1.0294960 67
## [188] 1.1137741 78
## [189] 1.1708907 82
## [190] 1.0133146 81
## [191] 1.0476055 83
## [192] 1.0366667 66
## [193] 1.0572574 89
## [194] 1.0749527 73
## [195] 1.3807611 126
## [196] 1.4405294 126
## [197] 1.1566116 72
## [198] 1.1044034 75
## [199] 1.0423783 73
## [200] 1.0917192 78
## [201] 1.0402230 78
## [202] 1.0023967 78
## [203] 1.0135506 76
## [204] 1.0962894 76
## [205] 1.1167727 79
## [206] 1.0694466 87
## [207] 1.3918881 128
## [208] 1.4447529 128
## [209] 1.0471380 80
## [210] 1.0638707 73
## [211] 1.0894078 84
## [212] 1.0770563 80
## [213] 1.0691367 90
## [214] 1.0864057 83
## [215] 1.1021572 92
## [216] 1.0952498 86
## [217] 1.3612795 130
## [218] 1.3551198 130
## [219] 1.0115930 78
## [220] 1.0664274 86
## [221] 1.0168262 82
## [222] 1.0078704 77
## [223] 1.3788748 119
## [224] 1.3693344 119
## [225] 1.0542373 88
## [226] 1.0532977 114
## [227] 0.9918255 87
## [228] 1.0131061 86
## [229] 1.0569244 80
## [230] 1.0127544 96
## [231] 1.3743687 140
## [232] 1.3057430 140
## [233] 1.1172287 98
## [234] 1.0720076 91
## [235] 1.0928953 92
## [236] 1.0630545 94
## [237] 1.0703247 106
## [238] 1.0551054 103
## [239] 1.3961841 160
## [240] 1.4106108 160
## [241] 1.0041693 103
## [242] 1.0678548 105
## [243] 1.0947710 103
## [244] 1.2000654 118
## [245] 1.1348912 114
## [246] 1.3874579 159
## [247] 1.3417175 159
## [248] 1.0841422 102
## [249] 1.1575428 113
## [250] 1.0971506 104
## [251] 1.3855509 148
## [252] 1.3449631 148
## [253] 1.1635315 107
## [254] 1.0804221 107
## [255] 1.3773893 152
## [256] 1.3813135 152
## [257] 1.0985487 129
## [258] 1.0248864 116
## [259] 1.3773893 152
## [260] 1.3139323 152
## [261] 1.0854708 129
## [262] 1.4059536 183
## [263] 1.4223272 183
## [264] 1.3895403 173
## [265] 1.3809450 173
## [266] 1.4136364 63
## [267] 1.6417798 63
## [268] 1.3822222 88
## [269] 1.6415055 88
## [270] 1.4136364 74
## [271] 1.5991939 74
## [272] 1.3950359 75
## [273] 1.6012769 75
## [274] 1.3973877 86
## [275] 1.6386974 86
## [276] 1.4136364 77
## [277] 1.5683924 77
## [278] 1.3963969 81
## [279] 1.5948718 81
## [280] 1.3970053 84
## [281] 1.6005882 84
## [282] 1.3791574 80
## [283] 1.5751820 80
## [284] 1.3982708 91
## [285] 1.6288814 91
## [286] 1.3977528 88
## [287] 1.5912553 88
## [288] 1.3928476 67
## [289] 1.5221711 67
## [290] 1.3928476 67
## [291] 1.4304740 67
## [292] 1.3952774 76
## [293] 1.5132367 76
## [294] 1.4136364 66
## [295] 1.4994521 66
## [296] 1.4136364 69
## [297] 1.5477659 69
## [298] 1.3726614 67
## [299] 1.3647051 67
## [300] 1.3701399 63
## [301] 1.3290598 63
## [302] 1.3316864 65
## [303] 1.2798354 65
## [304] 1.3945332 73
## [305] 1.4061068 73
## [306] 1.4136364 63
## [307] 1.2981515 63
## [308] 1.3918881 64
## [309] 1.3830867 64
## [310] 1.4136364 68
## [311] 1.3693344 68
## [312] 1.3803743 83
## [313] 1.2901984 83
## [314] 1.3915483 63
## [315] 1.3955128 63
## [316] 1.3743687 70
## [317] 1.2921415 70
## [318] 1.4136364 74
## [319] 1.3381011 74
## [320] 1.4136364 69
## [321] 1.3436649 69
## [322] 1.3754300 72
## [323] 1.3868450 72
## [324] 1.3952774 76
## [325] 1.4327453 76
## [326] 1.4136364 86
## [327] 1.4377251 86
## [328] 1.4136364 82
## [329] 1.4107820 82
## [330] 1.3769185 75
## [331] 1.2903493 75
## [332] 1.2276316 66
## [333] 1.3778481 77
## [334] 1.2995252 77
## [335] 1.1867565 68
## [336] 1.3764354 74
## [337] 1.2731447 74
## [338] 1.2603505 74
## [339] 1.4136364 89
## [340] 1.3365686 89
## [341] 1.3811390 85
## [342] 1.3212875 85
## [343] 1.3918881 64
## [344] 1.3035989 64
## [345] 1.1948593 71
## [346] 1.3782955 78
## [347] 1.3551198 78
## [348] 1.2440000 66
## [349] 1.3971987 85
## [350] 1.3329804 85
## [351] 1.3782955 78
## [352] 1.3290598 78
## [353] 1.2015909 68
## [354] 1.4136364 89
## [355] 1.4739729 89
## [356] 1.3787318 79
## [357] 1.3083580 79
## [358] 1.0637264 76
## [359] 1.3997772 101
## [360] 1.3874423 101
## [361] 1.3825674 89
## [362] 1.3596129 89
## [363] 1.3999117 102
## [364] 1.4011794 102
## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 102
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[7763 item(s), 1020 transaction(s)] done [0.01s].
## sorting and recoding items ... [108 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [310 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support confidence
## [1] {structur} => {network} 0.1058824 0.7883212
## [2] {optim} => {network} 0.1088235 0.7449664
## [3] {complex} => {network} 0.1009804 0.7202797
## [4] {input} => {network} 0.1137255 0.7837838
## [5] {learn} => {network} 0.1215686 0.7898089
## [6] {stateoftheart} => {network} 0.1205882 0.7068966
## [7] {classif} => {network} 0.1333333 0.7311828
## [8] {effici} => {network} 0.1294118 0.7252747
## [9] {design} => {network} 0.1313725 0.7322404
## [10] {appli} => {network} 0.1392157 0.7434555
## [11] {set} => {network} 0.1480392 0.7550000
## [12] {architectur} => {network} 0.1558824 0.7535545
## [13] {featur} => {network} 0.1509804 0.7368421
## [14] {inform} => {network} 0.1441176 0.7277228
## [15] {predict} => {network} 0.1558824 0.7098214
## [16] {compar} => {network} 0.1666667 0.7234043
## [17] {accuraci} => {network} 0.1735294 0.7108434
## [18] {problem} => {network} 0.1803922 0.7104247
## [19] {comput} => {network} 0.1911765 0.7303371
## [20] {demonstr} => {network} 0.1911765 0.7303371
## [21] {algorithm} => {network} 0.2000000 0.7285714
## [22] {convolut} => {neural} 0.2666667 0.8802589
## [23] {convolut} => {network} 0.2872549 0.9482201
## [24] {show} => {network} 0.2392157 0.7155425
## [25] {approach} => {network} 0.2509804 0.7191011
## [26] {method} => {network} 0.3176471 0.7089716
## [27] {propos} => {network} 0.3254902 0.7155172
## [28] {train} => {network} 0.3666667 0.7679671
## [29] {neural} => {network} 0.5500000 0.9606164
## [30] {network} => {neural} 0.5500000 0.8106936
## [31] {neural,classif} => {network} 0.1049020 0.9727273
## [32] {network,classif} => {neural} 0.1049020 0.7867647
## [33] {neural,effici} => {network} 0.1058824 0.9557522
## [34] {network,effici} => {neural} 0.1058824 0.8181818
## [35] {neural,design} => {network} 0.1049020 0.9469027
## [36] {network,design} => {neural} 0.1049020 0.7985075
## [37] {appli,neural} => {network} 0.1186275 0.9837398
## [38] {network,appli} => {neural} 0.1186275 0.8521127
## [39] {requir,neural} => {network} 0.1019608 0.9719626
## [40] {network,requir} => {neural} 0.1019608 0.7819549
## [41] {neural,detect} => {network} 0.1078431 0.9649123
## [42] {network,detect} => {neural} 0.1078431 0.8527132
## [43] {neural,larg} => {network} 0.1019608 0.9904762
## [44] {network,larg} => {neural} 0.1019608 0.7878788
## [45] {neural,set} => {network} 0.1215686 0.9687500
## [46] {network,set} => {neural} 0.1215686 0.8211921
## [47] {neural,techniqu} => {network} 0.1029412 0.9633028
## [48] {network,techniqu} => {neural} 0.1029412 0.8015267
## [49] {neural,machin} => {network} 0.1117647 0.9661017
## [50] {network,machin} => {neural} 0.1117647 0.8507463
## [51] {neural,architectur} => {network} 0.1205882 0.9248120
## [52] {network,architectur} => {neural} 0.1205882 0.7735849
## [53] {neural,process} => {network} 0.1058824 0.9391304
## [54] {network,process} => {neural} 0.1058824 0.8059701
## [55] {neural,featur} => {network} 0.1274510 0.9558824
## [56] {network,featur} => {neural} 0.1274510 0.8441558
## [57] {inform,neural} => {network} 0.1166667 0.9754098
## [58] {inform,network} => {neural} 0.1166667 0.8095238
## [59] {neural,task} => {network} 0.1205882 0.9461538
## [60] {network,task} => {neural} 0.1205882 0.8424658
## [61] {neural,signific} => {network} 0.1029412 0.9633028
## [62] {network,signific} => {neural} 0.1029412 0.7954545
## [63] {neural,provid} => {network} 0.1225490 0.9842520
## [64] {network,provid} => {neural} 0.1225490 0.8503401
## [65] {develop,neural} => {network} 0.1009804 0.9363636
## [66] {develop,network} => {neural} 0.1009804 0.8110236
## [67] {neural,studi} => {network} 0.1176471 0.9836066
## [68] {network,studi} => {neural} 0.1176471 0.8510638
## [69] {neural,challeng} => {network} 0.1107843 0.9741379
## [70] {network,challeng} => {neural} 0.1107843 0.7739726
## [71] {neural,time} => {network} 0.1127451 0.9583333
## [72] {network,time} => {neural} 0.1127451 0.7770270
## [73] {neural,predict} => {network} 0.1372549 0.9655172
## [74] {network,predict} => {neural} 0.1372549 0.8805031
## [75] {recent,neural} => {network} 0.1196078 0.9682540
## [76] {network,recent} => {neural} 0.1196078 0.7820513
## [77] {high,neural} => {network} 0.1284314 0.9632353
## [78] {high,network} => {neural} 0.1284314 0.8187500
## [79] {train,compar} => {network} 0.1019608 0.8125000
## [80] {neural,compar} => {network} 0.1303922 0.9637681
## [81] {network,compar} => {neural} 0.1303922 0.7823529
## [82] {neural,work} => {network} 0.1264706 0.9347826
## [83] {network,work} => {neural} 0.1264706 0.7771084
## [84] {neural,accuraci} => {network} 0.1441176 0.9800000
## [85] {network,accuraci} => {neural} 0.1441176 0.8305085
## [86] {problem,data} => {network} 0.1000000 0.7234043
## [87] {neural,problem} => {network} 0.1519608 0.9810127
## [88] {network,problem} => {neural} 0.1519608 0.8423913
## [89] {neural,achiev} => {network} 0.1392157 0.9726027
## [90] {network,achiev} => {neural} 0.1392157 0.8255814
## [91] {neural,applic} => {network} 0.1431373 0.9733333
## [92] {network,applic} => {neural} 0.1431373 0.8742515
## [93] {improv,perform} => {network} 0.1029412 0.7191781
## [94] {propos,improv} => {network} 0.1009804 0.7573529
## [95] {neural,improv} => {network} 0.1362745 0.9720280
## [96] {network,improv} => {neural} 0.1362745 0.7942857
## [97] {neural,present} => {network} 0.1470588 0.9493671
## [98] {network,present} => {neural} 0.1470588 0.8241758
## [99] {system,neural} => {network} 0.1441176 0.9800000
## [100] {network,system} => {neural} 0.1441176 0.8121547
## [101] {propos,comput} => {network} 0.1009804 0.8046875
## [102] {comput,train} => {network} 0.1039216 0.8217054
## [103] {neural,comput} => {network} 0.1450980 0.9610390
## [104] {network,comput} => {neural} 0.1450980 0.7589744
## [105] {demonstr,train} => {network} 0.1058824 0.8307692
## [106] {neural,demonstr} => {network} 0.1558824 0.9636364
## [107] {network,demonstr} => {neural} 0.1558824 0.8153846
## [108] {propos,algorithm} => {network} 0.1009804 0.7518248
## [109] {train,algorithm} => {neural} 0.1009804 0.7054795
## [110] {train,algorithm} => {network} 0.1127451 0.7876712
## [111] {data,algorithm} => {network} 0.1009804 0.7410072
## [112] {neural,algorithm} => {network} 0.1686275 0.9662921
## [113] {network,algorithm} => {neural} 0.1686275 0.8431373
## [114] {dataset,result} => {network} 0.1000000 0.7183099
## [115] {dataset,propos} => {network} 0.1068627 0.7266667
## [116] {dataset,train} => {network} 0.1264706 0.7865854
## [117] {dataset,neural} => {network} 0.1539216 0.9289941
## [118] {dataset,network} => {neural} 0.1539216 0.7850000
## [119] {show,convolut} => {network} 0.1019608 0.9719626
## [120] {convolut,approach} => {neural} 0.1000000 0.9026549
## [121] {convolut,approach} => {network} 0.1049020 0.9469027
## [122] {imag,convolut} => {neural} 0.1323529 0.8709677
## [123] {imag,convolut} => {network} 0.1470588 0.9677419
## [124] {convolut,paper} => {network} 0.1058824 0.9642857
## [125] {convolut,result} => {neural} 0.1215686 0.8857143
## [126] {convolut,result} => {network} 0.1333333 0.9714286
## [127] {convolut,perform} => {neural} 0.1323529 0.9000000
## [128] {convolut,perform} => {network} 0.1401961 0.9533333
## [129] {method,convolut} => {neural} 0.1343137 0.9256757
## [130] {method,convolut} => {network} 0.1382353 0.9527027
## [131] {propos,convolut} => {neural} 0.1284314 0.8851351
## [132] {propos,convolut} => {network} 0.1392157 0.9594595
## [133] {convolut,train} => {neural} 0.1509804 0.9005848
## [134] {convolut,train} => {network} 0.1617647 0.9649123
## [135] {convolut,data} => {neural} 0.1303922 0.9172414
## [136] {convolut,data} => {network} 0.1372549 0.9655172
## [137] {model,convolut} => {neural} 0.1137255 0.8405797
## [138] {model,convolut} => {network} 0.1274510 0.9420290
## [139] {neural,convolut} => {network} 0.2598039 0.9742647
## [140] {network,convolut} => {neural} 0.2598039 0.9044369
## [141] {method,base} => {network} 0.1068627 0.7218543
## [142] {propos,base} => {network} 0.1156863 0.7023810
## [143] {base,train} => {network} 0.1205882 0.7592593
## [144] {neural,base} => {network} 0.1754902 0.9728261
## [145] {network,base} => {neural} 0.1754902 0.8443396
## [146] {show,paper} => {network} 0.1009804 0.7006803
## [147] {show,perform} => {network} 0.1176471 0.7100592
## [148] {method,show} => {network} 0.1225490 0.7812500
## [149] {propos,show} => {network} 0.1284314 0.7485714
## [150] {show,train} => {network} 0.1294118 0.7630058
## [151] {show,data} => {network} 0.1196078 0.7093023
## [152] {model,show} => {network} 0.1205882 0.7109827
## [153] {neural,show} => {network} 0.1990196 0.9712919
## [154] {network,show} => {neural} 0.1990196 0.8319672
## [155] {paper,approach} => {network} 0.1039216 0.7260274
## [156] {result,approach} => {network} 0.1245098 0.7341040
## [157] {perform,approach} => {network} 0.1294118 0.7719298
## [158] {method,approach} => {network} 0.1303922 0.7600000
## [159] {propos,approach} => {network} 0.1401961 0.7566138
## [160] {train,approach} => {network} 0.1382353 0.8150289
## [161] {data,approach} => {network} 0.1333333 0.7234043
## [162] {neural,approach} => {network} 0.2078431 0.9680365
## [163] {network,approach} => {neural} 0.2078431 0.8281250
## [164] {imag,paper} => {network} 0.1009804 0.7202797
## [165] {imag,result} => {network} 0.1235294 0.7368421
## [166] {imag,perform} => {network} 0.1303922 0.7307692
## [167] {imag,method} => {network} 0.1382353 0.7121212
## [168] {imag,propos} => {network} 0.1225490 0.7440476
## [169] {imag,train} => {network} 0.1529412 0.7684729
## [170] {imag,model} => {network} 0.1137255 0.7030303
## [171] {imag,neural} => {network} 0.1980392 0.9573460
## [172] {imag,network} => {neural} 0.1980392 0.7859922
## [173] {paper,result} => {network} 0.1362745 0.7202073
## [174] {paper,perform} => {network} 0.1313725 0.7089947
## [175] {method,paper} => {network} 0.1254902 0.7032967
## [176] {propos,paper} => {network} 0.1421569 0.7107843
## [177] {paper,train} => {network} 0.1196078 0.7484663
## [178] {neural,paper} => {network} 0.2098039 0.9683258
## [179] {network,paper} => {neural} 0.2098039 0.7925926
## [180] {result,perform} => {network} 0.1529412 0.7090909
## [181] {method,result} => {network} 0.1441176 0.7067308
## [182] {propos,result} => {network} 0.1588235 0.7043478
## [183] {result,train} => {network} 0.1696078 0.7757848
## [184] {data,result} => {network} 0.1509804 0.7264151
## [185] {neural,result} => {network} 0.2382353 0.9681275
## [186] {network,result} => {neural} 0.2382353 0.7813505
## [187] {method,perform} => {network} 0.1490196 0.7169811
## [188] {propos,perform} => {network} 0.1509804 0.7162791
## [189] {perform,train} => {network} 0.1715686 0.7510730
## [190] {data,perform} => {network} 0.1666667 0.7083333
## [191] {neural,perform} => {network} 0.2539216 0.9628253
## [192] {network,perform} => {neural} 0.2539216 0.8170347
## [193] {propos,method} => {network} 0.1862745 0.7450980
## [194] {method,train} => {network} 0.1941176 0.7888446
## [195] {method,data} => {network} 0.1637255 0.7106383
## [196] {method,neural} => {network} 0.2578431 0.9633700
## [197] {network,method} => {neural} 0.2578431 0.8117284
## [198] {propos,train} => {network} 0.1784314 0.7777778
## [199] {propos,data} => {network} 0.1666667 0.7391304
## [200] {propos,neural} => {network} 0.2588235 0.9600000
## [201] {network,propos} => {neural} 0.2588235 0.7951807
## [202] {data,train} => {network} 0.1980392 0.7453875
## [203] {model,train} => {network} 0.1813725 0.7283465
## [204] {neural,train} => {network} 0.3009804 0.9715190
## [205] {network,train} => {neural} 0.3009804 0.8208556
## [206] {neural,data} => {network} 0.2735294 0.9687500
## [207] {network,data} => {neural} 0.2735294 0.8110465
## [208] {model,neural} => {network} 0.2696078 0.9649123
## [209] {model,network} => {neural} 0.2696078 0.8184524
## [210] {imag,neural,convolut} => {network} 0.1294118 0.9777778
## [211] {imag,network,convolut} => {neural} 0.1294118 0.8800000
## [212] {neural,convolut,result} => {network} 0.1186275 0.9758065
## [213] {network,convolut,result} => {neural} 0.1186275 0.8897059
## [214] {neural,convolut,perform} => {network} 0.1313725 0.9925926
## [215] {network,convolut,perform} => {neural} 0.1313725 0.9370629
## [216] {method,neural,convolut} => {network} 0.1284314 0.9562044
## [217] {network,method,convolut} => {neural} 0.1284314 0.9290780
## [218] {propos,neural,convolut} => {network} 0.1245098 0.9694656
## [219] {network,propos,convolut} => {neural} 0.1245098 0.8943662
## [220] {neural,convolut,train} => {network} 0.1480392 0.9805195
## [221] {network,convolut,train} => {neural} 0.1480392 0.9151515
## [222] {neural,convolut,data} => {network} 0.1274510 0.9774436
## [223] {network,convolut,data} => {neural} 0.1274510 0.9285714
## [224] {model,neural,convolut} => {network} 0.1117647 0.9827586
## [225] {model,network,convolut} => {neural} 0.1117647 0.8769231
## [226] {neural,base,train} => {network} 0.1058824 0.9729730
## [227] {network,base,train} => {neural} 0.1058824 0.8780488
## [228] {neural,show,perform} => {network} 0.1000000 0.9714286
## [229] {network,show,perform} => {neural} 0.1000000 0.8500000
## [230] {propos,neural,show} => {network} 0.1019608 0.9719626
## [231] {network,propos,show} => {neural} 0.1019608 0.7938931
## [232] {neural,show,train} => {network} 0.1088235 0.9823009
## [233] {network,show,train} => {neural} 0.1088235 0.8409091
## [234] {neural,show,data} => {network} 0.1029412 0.9722222
## [235] {network,show,data} => {neural} 0.1029412 0.8606557
## [236] {model,neural,show} => {network} 0.1019608 0.9719626
## [237] {model,network,show} => {neural} 0.1019608 0.8455285
## [238] {neural,perform,approach} => {network} 0.1078431 0.9821429
## [239] {network,perform,approach} => {neural} 0.1078431 0.8333333
## [240] {method,neural,approach} => {network} 0.1068627 0.9646018
## [241] {network,method,approach} => {neural} 0.1068627 0.8195489
## [242] {propos,neural,approach} => {network} 0.1196078 0.9760000
## [243] {network,propos,approach} => {neural} 0.1196078 0.8531469
## [244] {neural,train,approach} => {network} 0.1137255 0.9747899
## [245] {network,train,approach} => {neural} 0.1137255 0.8226950
## [246] {neural,data,approach} => {network} 0.1127451 0.9663866
## [247] {network,data,approach} => {neural} 0.1127451 0.8455882
## [248] {model,neural,approach} => {network} 0.1009804 0.9537037
## [249] {model,network,approach} => {neural} 0.1009804 0.8442623
## [250] {imag,neural,perform} => {network} 0.1068627 0.9561404
## [251] {imag,network,perform} => {neural} 0.1068627 0.8195489
## [252] {imag,method,neural} => {network} 0.1088235 0.9568966
## [253] {imag,network,method} => {neural} 0.1088235 0.7872340
## [254] {imag,neural,train} => {network} 0.1245098 0.9694656
## [255] {imag,network,train} => {neural} 0.1245098 0.8141026
## [256] {neural,paper,result} => {network} 0.1039216 0.9724771
## [257] {network,paper,result} => {neural} 0.1039216 0.7625899
## [258] {neural,paper,perform} => {network} 0.1039216 0.9724771
## [259] {network,paper,perform} => {neural} 0.1039216 0.7910448
## [260] {propos,neural,paper} => {network} 0.1049020 0.9639640
## [261] {network,propos,paper} => {neural} 0.1049020 0.7379310
## [262] {neural,data,paper} => {network} 0.1009804 0.9626168
## [263] {network,data,paper} => {neural} 0.1009804 0.7984496
## [264] {model,neural,paper} => {network} 0.1058824 0.9642857
## [265] {model,network,paper} => {neural} 0.1058824 0.8181818
## [266] {neural,result,perform} => {network} 0.1215686 0.9612403
## [267] {network,result,perform} => {neural} 0.1215686 0.7948718
## [268] {method,neural,result} => {network} 0.1127451 0.9663866
## [269] {network,method,result} => {neural} 0.1127451 0.7823129
## [270] {propos,neural,result} => {network} 0.1176471 0.9600000
## [271] {network,propos,result} => {neural} 0.1176471 0.7407407
## [272] {neural,result,train} => {network} 0.1362745 0.9788732
## [273] {network,result,train} => {neural} 0.1362745 0.8034682
## [274] {neural,data,result} => {network} 0.1166667 0.9754098
## [275] {network,data,result} => {neural} 0.1166667 0.7727273
## [276] {model,neural,result} => {network} 0.1196078 0.9682540
## [277] {model,network,result} => {neural} 0.1196078 0.7973856
## [278] {method,neural,perform} => {network} 0.1205882 0.9761905
## [279] {network,method,perform} => {neural} 0.1205882 0.8092105
## [280] {propos,neural,perform} => {network} 0.1176471 0.9523810
## [281] {network,propos,perform} => {neural} 0.1176471 0.7792208
## [282] {data,perform,train} => {network} 0.1039216 0.7681159
## [283] {neural,perform,train} => {network} 0.1421569 0.9731544
## [284] {network,perform,train} => {neural} 0.1421569 0.8285714
## [285] {neural,data,perform} => {network} 0.1372549 0.9790210
## [286] {network,data,perform} => {neural} 0.1372549 0.8235294
## [287] {model,neural,perform} => {network} 0.1225490 0.9689922
## [288] {model,network,perform} => {neural} 0.1225490 0.8278146
## [289] {propos,method,train} => {network} 0.1137255 0.8000000
## [290] {propos,method,neural} => {network} 0.1460784 0.9738562
## [291] {network,propos,method} => {neural} 0.1460784 0.7842105
## [292] {method,data,train} => {network} 0.1078431 0.7638889
## [293] {method,neural,train} => {network} 0.1617647 0.9821429
## [294] {network,method,train} => {neural} 0.1617647 0.8333333
## [295] {method,neural,data} => {network} 0.1362745 0.9720280
## [296] {network,method,data} => {neural} 0.1362745 0.8323353
## [297] {model,method,neural} => {network} 0.1245098 0.9694656
## [298] {model,network,method} => {neural} 0.1245098 0.8141026
## [299] {propos,neural,train} => {network} 0.1470588 0.9740260
## [300] {network,propos,train} => {neural} 0.1470588 0.8241758
## [301] {propos,neural,data} => {network} 0.1382353 0.9791667
## [302] {network,propos,data} => {neural} 0.1382353 0.8294118
## [303] {model,propos,neural} => {network} 0.1264706 0.9772727
## [304] {model,network,propos} => {neural} 0.1264706 0.8062500
## [305] {neural,data,train} => {network} 0.1607843 0.9647059
## [306] {network,data,train} => {neural} 0.1607843 0.8118812
## [307] {model,neural,train} => {network} 0.1490196 0.9743590
## [308] {model,network,train} => {neural} 0.1490196 0.8216216
## [309] {model,neural,data} => {network} 0.1421569 0.9666667
## [310] {model,network,data} => {neural} 0.1421569 0.8011050
## lift count
## [1] 1.161976 108
## [2] 1.098072 111
## [3] 1.061684 103
## [4] 1.155288 116
## [5] 1.164169 124
## [6] 1.041957 123
## [7] 1.077755 136
## [8] 1.069047 132
## [9] 1.079314 134
## [10] 1.095845 142
## [11] 1.112861 151
## [12] 1.110731 159
## [13] 1.086097 154
## [14] 1.072655 147
## [15] 1.046269 159
## [16] 1.066290 170
## [17] 1.047775 177
## [18] 1.047158 184
## [19] 1.076508 195
## [20] 1.076508 195
## [21] 1.073906 204
## [22] 1.537438 272
## [23] 1.397665 293
## [24] 1.054701 244
## [25] 1.059947 256
## [26] 1.045016 324
## [27] 1.054664 332
## [28] 1.131975 374
## [29] 1.415938 561
## [30] 1.415938 561
## [31] 1.433789 107
## [32] 1.374144 107
## [33] 1.408768 108
## [34] 1.429016 108
## [35] 1.395724 107
## [36] 1.394653 107
## [37] 1.450021 121
## [38] 1.488279 121
## [39] 1.432662 104
## [40] 1.365743 104
## [41] 1.422270 110
## [42] 1.489328 110
## [43] 1.459950 104
## [44] 1.376090 104
## [45] 1.427926 124
## [46] 1.434274 124
## [47] 1.419897 105
## [48] 1.399927 105
## [49] 1.424023 114
## [50] 1.485892 114
## [51] 1.363162 123
## [52] 1.351124 123
## [53] 1.384267 108
## [54] 1.407688 108
## [55] 1.408960 130
## [56] 1.474382 130
## [57] 1.437743 119
## [58] 1.413894 119
## [59] 1.394620 123
## [60] 1.471430 123
## [61] 1.419897 105
## [62] 1.389321 105
## [63] 1.450776 125
## [64] 1.485183 125
## [65] 1.380189 103
## [66] 1.416514 103
## [67] 1.449825 120
## [68] 1.486447 120
## [69] 1.435868 113
## [70] 1.351801 113
## [71] 1.412572 115
## [72] 1.357136 115
## [73] 1.423161 140
## [74] 1.537865 140
## [75] 1.427195 122
## [76] 1.365911 122
## [77] 1.419798 131
## [78] 1.430009 131
## [79] 1.197616 104
## [80] 1.420583 133
## [81] 1.366438 133
## [82] 1.377859 129
## [83] 1.357278 129
## [84] 1.444509 147
## [85] 1.450546 147
## [86] 1.066290 102
## [87] 1.446001 155
## [88] 1.471300 155
## [89] 1.433605 142
## [90] 1.441940 142
## [91] 1.434682 146
## [92] 1.526946 146
## [93] 1.060060 105
## [94] 1.116329 103
## [95] 1.432758 139
## [96] 1.387280 139
## [97] 1.399356 150
## [98] 1.439485 150
## [99] 1.444509 147
## [100] 1.418489 147
## [101] 1.186100 103
## [102] 1.211184 106
## [103] 1.416560 148
## [104] 1.325606 148
## [105] 1.224544 108
## [106] 1.420389 159
## [107] 1.424131 159
## [108] 1.108181 103
## [109] 1.232173 103
## [110] 1.161018 115
## [111] 1.092236 103
## [112] 1.424303 172
## [113] 1.472603 172
## [114] 1.058780 102
## [115] 1.071098 109
## [116] 1.159418 129
## [117] 1.369327 157
## [118] 1.371062 157
## [119] 1.432662 104
## [120] 1.576555 102
## [121] 1.395724 107
## [122] 1.521211 135
## [123] 1.426440 150
## [124] 1.421346 108
## [125] 1.546967 124
## [126] 1.431874 136
## [127] 1.571918 135
## [128] 1.405202 143
## [129] 1.616762 137
## [130] 1.404273 141
## [131] 1.545955 131
## [132] 1.414232 142
## [133] 1.572939 154
## [134] 1.422270 165
## [135] 1.602031 133
## [136] 1.423161 140
## [137] 1.468136 116
## [138] 1.388540 130
## [139] 1.436055 265
## [140] 1.579667 265
## [141] 1.064005 109
## [142] 1.035301 118
## [143] 1.119139 123
## [144] 1.433934 179
## [145] 1.474703 179
## [146] 1.032795 103
## [147] 1.046619 120
## [148] 1.151553 125
## [149] 1.103386 131
## [150] 1.124662 132
## [151] 1.045503 122
## [152] 1.047980 123
## [153] 1.431673 203
## [154] 1.453093 203
## [155] 1.070156 106
## [156] 1.082061 127
## [157] 1.137816 132
## [158] 1.120231 133
## [159] 1.115240 143
## [160] 1.201343 141
## [161] 1.066290 136
## [162] 1.426875 212
## [163] 1.446383 212
## [164] 1.061684 103
## [165] 1.086097 126
## [166] 1.077145 133
## [167] 1.049658 141
## [168] 1.096718 125
## [169] 1.132720 156
## [170] 1.036259 116
## [171] 1.411117 202
## [172] 1.372795 202
## [173] 1.061577 139
## [174] 1.045050 134
## [175] 1.036651 128
## [176] 1.047688 145
## [177] 1.103231 122
## [178] 1.427301 214
## [179] 1.384323 214
## [180] 1.045192 156
## [181] 1.041713 147
## [182] 1.038201 162
## [183] 1.143498 173
## [184] 1.070727 154
## [185] 1.427009 243
## [186] 1.364687 243
## [187] 1.056822 152
## [188] 1.055787 154
## [189] 1.107073 175
## [190] 1.044075 170
## [191] 1.419193 259
## [192] 1.427013 259
## [193] 1.098266 190
## [194] 1.162748 198
## [195] 1.047473 167
## [196] 1.419996 263
## [197] 1.417745 263
## [198] 1.146435 182
## [199] 1.089470 170
## [200] 1.415029 264
## [201] 1.388843 264
## [202] 1.098692 202
## [203] 1.073574 185
## [204] 1.432008 307
## [205] 1.433686 307
## [206] 1.427926 279
## [207] 1.416554 279
## [208] 1.422270 275
## [209] 1.429489 275
## [210] 1.441233 132
## [211] 1.536986 132
## [212] 1.438327 121
## [213] 1.553938 121
## [214] 1.463070 134
## [215] 1.636651 134
## [216] 1.409434 131
## [217] 1.622705 131
## [218] 1.428981 127
## [219] 1.562078 127
## [220] 1.445274 151
## [221] 1.598381 151
## [222] 1.440741 130
## [223] 1.621820 130
## [224] 1.448575 114
## [225] 1.531612 114
## [226] 1.434151 108
## [227] 1.533578 108
## [228] 1.431874 102
## [229] 1.484589 102
## [230] 1.432662 104
## [231] 1.386594 104
## [232] 1.447900 111
## [233] 1.468711 111
## [234] 1.433044 105
## [235] 1.503200 105
## [236] 1.432662 104
## [237] 1.476779 104
## [238] 1.447667 110
## [239] 1.455479 110
## [240] 1.421812 109
## [241] 1.431404 109
## [242] 1.438613 122
## [243] 1.490085 122
## [244] 1.436829 116
## [245] 1.436899 116
## [246] 1.424443 115
## [247] 1.476884 115
## [248] 1.405748 103
## [249] 1.474568 103
## [250] 1.409340 109
## [251] 1.431404 109
## [252] 1.410454 111
## [253] 1.374964 111
## [254] 1.428981 127
## [255] 1.421891 127
## [256] 1.433420 106
## [257] 1.331921 106
## [258] 1.433420 106
## [259] 1.381619 106
## [260] 1.420872 107
## [261] 1.288852 107
## [262] 1.418886 103
## [263] 1.394552 103
## [264] 1.421346 108
## [265] 1.429016 108
## [266] 1.416857 124
## [267] 1.388303 124
## [268] 1.424443 115
## [269] 1.366368 115
## [270] 1.415029 120
## [271] 1.293760 120
## [272] 1.442848 139
## [273] 1.403318 139
## [274] 1.437743 119
## [275] 1.349626 119
## [276] 1.427195 122
## [277] 1.392694 122
## [278] 1.438893 123
## [279] 1.413347 123
## [280] 1.403799 120
## [281] 1.360968 120
## [282] 1.132194 106
## [283] 1.434418 145
## [284] 1.447162 145
## [285] 1.443066 140
## [286] 1.438356 140
## [287] 1.428283 125
## [288] 1.445841 125
## [289] 1.179191 116
## [290] 1.435453 149
## [291] 1.369683 149
## [292] 1.125963 110
## [293] 1.447667 165
## [294] 1.455479 165
## [295] 1.432758 139
## [296] 1.453736 139
## [297] 1.428981 127
## [298] 1.421891 127
## [299] 1.435703 150
## [300] 1.439485 150
## [301] 1.443280 141
## [302] 1.448630 141
## [303] 1.440489 129
## [304] 1.408176 129
## [305] 1.421965 164
## [306] 1.418012 164
## [307] 1.436194 152
## [308] 1.435024 152
## [309] 1.424855 145
## [310] 1.399190 145
## Warning in asMethod(object): matrix contains values other than 0 and 1!
## Setting all entries != 0 to 1.
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.7 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 202
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[11053 item(s), 2027 transaction(s)] done [0.01s].
## sorting and recoding items ... [106 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [349 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
## lhs rhs support confidence
## [1] {cnn} => {neural} 0.1006413 0.8908297
## [2] {cnn} => {network} 0.1060681 0.9388646
## [3] {layer} => {network} 0.1080414 0.8390805
## [4] {general} => {network} 0.1031080 0.7491039
## [5] {function} => {network} 0.1006413 0.7391304
## [6] {input} => {network} 0.1159349 0.8131488
## [7] {complex} => {network} 0.1050814 0.7421603
## [8] {number} => {network} 0.1045881 0.7138047
## [9] {optim} => {network} 0.1124815 0.7549669
## [10] {test} => {network} 0.1149482 0.7147239
## [11] {design} => {network} 0.1188949 0.7259036
## [12] {effici} => {network} 0.1262950 0.7485380
## [13] {appli} => {network} 0.1346818 0.7438692
## [14] {stateoftheart} => {network} 0.1327084 0.7270270
## [15] {learn} => {network} 0.1356685 0.7452575
## [16] {larg} => {network} 0.1332018 0.7031250
## [17] {set} => {network} 0.1484953 0.7678571
## [18] {inform} => {network} 0.1366552 0.7139175
## [19] {studi} => {network} 0.1406019 0.7107232
## [20] {time} => {network} 0.1440553 0.7373737
## [21] {classif} => {network} 0.1593488 0.7511628
## [22] {architectur} => {network} 0.1741490 0.7775330
## [23] {predict} => {network} 0.1593488 0.7258427
## [24] {compar} => {network} 0.1618155 0.7404063
## [25] {task} => {network} 0.1682289 0.7002053
## [26] {problem} => {network} 0.1736556 0.7082495
## [27] {accuraci} => {network} 0.1780957 0.7307692
## [28] {featur} => {network} 0.1854958 0.7387033
## [29] {achiev} => {network} 0.1859891 0.7208413
## [30] {demonstr} => {network} 0.1874692 0.7265774
## [31] {algorithm} => {network} 0.1958559 0.7179024
## [32] {comput} => {network} 0.2017760 0.7316637
## [33] {dataset} => {network} 0.2022694 0.7032590
## [34] {convolut} => {neural} 0.2738037 0.8604651
## [35] {convolut} => {network} 0.3034040 0.9534884
## [36] {imag} => {network} 0.2580168 0.7294282
## [37] {show} => {network} 0.2639369 0.7338820
## [38] {approach} => {network} 0.2570301 0.7117486
## [39] {method} => {network} 0.3108041 0.7110609
## [40] {result} => {network} 0.3117908 0.7117117
## [41] {perform} => {network} 0.3152442 0.7092120
## [42] {propos} => {network} 0.3260977 0.7344444
## [43] {train} => {network} 0.3660582 0.7713098
## [44] {data} => {network} 0.3285644 0.7040169
## [45] {neural} => {network} 0.5510607 0.9679376
## [46] {network} => {neural} 0.5510607 0.7933239
## [47] {appli,neural} => {network} 0.1105081 0.9824561
## [48] {network,appli} => {neural} 0.1105081 0.8205128
## [49] {neural,learn} => {network} 0.1040947 0.9723502
## [50] {network,learn} => {neural} 0.1040947 0.7672727
## [51] {neural,detect} => {network} 0.1016280 0.9716981
## [52] {network,detect} => {neural} 0.1016280 0.8207171
## [53] {neural,experi} => {network} 0.1050814 0.9770642
## [54] {network,experi} => {neural} 0.1050814 0.8129771
## [55] {evalu,neural} => {network} 0.1001480 0.9712919
## [56] {evalu,network} => {neural} 0.1001480 0.7518519
## [57] {neural,larg} => {network} 0.1050814 0.9953271
## [58] {network,larg} => {neural} 0.1050814 0.7888889
## [59] {neural,set} => {network} 0.1149482 0.9708333
## [60] {network,set} => {neural} 0.1149482 0.7740864
## [61] {inform,neural} => {network} 0.1100148 0.9695652
## [62] {inform,network} => {neural} 0.1100148 0.8050542
## [63] {neural,provid} => {network} 0.1075481 0.9819820
## [64] {network,provid} => {neural} 0.1075481 0.8074074
## [65] {neural,techniqu} => {network} 0.1050814 0.9770642
## [66] {network,techniqu} => {neural} 0.1050814 0.8007519
## [67] {neural,studi} => {network} 0.1154415 0.9831933
## [68] {network,studi} => {neural} 0.1154415 0.8210526
## [69] {neural,time} => {network} 0.1075481 0.9688889
## [70] {network,time} => {neural} 0.1075481 0.7465753
## [71] {neural,signific} => {network} 0.1045881 0.9680365
## [72] {network,signific} => {neural} 0.1045881 0.7940075
## [73] {neural,classif} => {network} 0.1272817 0.9699248
## [74] {network,classif} => {neural} 0.1272817 0.7987616
## [75] {neural,process} => {network} 0.1139615 0.9585062
## [76] {network,process} => {neural} 0.1139615 0.8133803
## [77] {develop,neural} => {network} 0.1065614 0.9600000
## [78] {develop,network} => {neural} 0.1065614 0.8089888
## [79] {neural,machin} => {network} 0.1208683 0.9760956
## [80] {network,machin} => {neural} 0.1208683 0.8390411
## [81] {neural,challeng} => {network} 0.1105081 0.9781659
## [82] {network,challeng} => {neural} 0.1105081 0.7724138
## [83] {neural,architectur} => {network} 0.1287617 0.9490909
## [84] {network,architectur} => {neural} 0.1287617 0.7393768
## [85] {recent,neural} => {network} 0.1134682 0.9745763
## [86] {network,recent} => {neural} 0.1134682 0.7718121
## [87] {model,predict} => {network} 0.1011347 0.7044674
## [88] {neural,predict} => {network} 0.1317218 0.9744526
## [89] {network,predict} => {neural} 0.1317218 0.8266254
## [90] {neural,compar} => {network} 0.1223483 0.9725490
## [91] {network,compar} => {neural} 0.1223483 0.7560976
## [92] {high,neural} => {network} 0.1203749 0.9721116
## [93] {high,network} => {neural} 0.1203749 0.8000000
## [94] {neural,work} => {network} 0.1312284 0.9568345
## [95] {network,work} => {neural} 0.1312284 0.7800587
## [96] {neural,task} => {network} 0.1366552 0.9651568
## [97] {network,task} => {neural} 0.1366552 0.8123167
## [98] {neural,problem} => {network} 0.1406019 0.9793814
## [99] {network,problem} => {neural} 0.1406019 0.8096591
## [100] {neural,applic} => {network} 0.1361618 0.9857143
## [101] {network,applic} => {neural} 0.1361618 0.8440367
## [102] {model,accuraci} => {network} 0.1001480 0.7463235
## [103] {neural,accuraci} => {network} 0.1425752 0.9796610
## [104] {network,accuraci} => {neural} 0.1425752 0.8005540
## [105] {improv,perform} => {network} 0.1070548 0.7508651
## [106] {neural,improv} => {network} 0.1336951 0.9575972
## [107] {network,improv} => {neural} 0.1336951 0.7655367
## [108] {neural,featur} => {network} 0.1504687 0.9744409
## [109] {network,featur} => {neural} 0.1504687 0.8111702
## [110] {neural,present} => {network} 0.1475086 0.9676375
## [111] {network,present} => {neural} 0.1475086 0.8037634
## [112] {perform,achiev} => {network} 0.1031080 0.7256944
## [113] {neural,achiev} => {network} 0.1475086 0.9676375
## [114] {network,achiev} => {neural} 0.1475086 0.7931034
## [115] {system,neural} => {network} 0.1396152 0.9725086
## [116] {network,system} => {neural} 0.1396152 0.8108883
## [117] {demonstr,train} => {network} 0.1040947 0.7992424
## [118] {neural,demonstr} => {network} 0.1480020 0.9646302
## [119] {network,demonstr} => {neural} 0.1480020 0.7894737
## [120] {train,algorithm} => {network} 0.1105081 0.7915194
## [121] {neural,algorithm} => {network} 0.1558954 0.9604863
## [122] {network,algorithm} => {neural} 0.1558954 0.7959698
## [123] {propos,comput} => {network} 0.1036014 0.8076923
## [124] {comput,train} => {network} 0.1080414 0.8081181
## [125] {model,comput} => {network} 0.1050814 0.7500000
## [126] {neural,comput} => {network} 0.1563888 0.9753846
## [127] {network,comput} => {neural} 0.1563888 0.7750611
## [128] {dataset,convolut} => {network} 0.1065614 0.9515419
## [129] {dataset,result} => {network} 0.1036014 0.7317073
## [130] {dataset,propos} => {network} 0.1100148 0.7335526
## [131] {dataset,train} => {network} 0.1253083 0.7627628
## [132] {dataset,data} => {network} 0.1026147 0.7098976
## [133] {dataset,neural} => {network} 0.1578688 0.9495549
## [134] {dataset,network} => {neural} 0.1578688 0.7804878
## [135] {convolut,base} => {neural} 0.1026147 0.8776371
## [136] {convolut,base} => {network} 0.1129748 0.9662447
## [137] {base,approach} => {network} 0.1006413 0.7527675
## [138] {method,base} => {network} 0.1100148 0.7263844
## [139] {base,perform} => {network} 0.1045881 0.7090301
## [140] {propos,base} => {network} 0.1159349 0.7253086
## [141] {base,train} => {network} 0.1223483 0.7750000
## [142] {data,base} => {network} 0.1085348 0.7284768
## [143] {neural,base} => {network} 0.1835224 0.9738220
## [144] {network,base} => {neural} 0.1835224 0.8104575
## [145] {imag,convolut} => {neural} 0.1425752 0.8704819
## [146] {imag,convolut} => {network} 0.1573754 0.9608434
## [147] {show,convolut} => {neural} 0.1016280 0.8512397
## [148] {show,convolut} => {network} 0.1149482 0.9628099
## [149] {convolut,approach} => {neural} 0.1090281 0.8911290
## [150] {convolut,approach} => {network} 0.1169216 0.9556452
## [151] {convolut,paper} => {neural} 0.1110015 0.8587786
## [152] {convolut,paper} => {network} 0.1253083 0.9694656
## [153] {method,convolut} => {neural} 0.1341885 0.8918033
## [154] {method,convolut} => {network} 0.1455353 0.9672131
## [155] {convolut,result} => {neural} 0.1302417 0.8712871
## [156] {convolut,result} => {network} 0.1450419 0.9702970
## [157] {convolut,perform} => {neural} 0.1332018 0.8598726
## [158] {convolut,perform} => {network} 0.1484953 0.9585987
## [159] {propos,convolut} => {neural} 0.1272817 0.8686869
## [160] {propos,convolut} => {network} 0.1420819 0.9696970
## [161] {convolut,train} => {neural} 0.1475086 0.8742690
## [162] {convolut,train} => {network} 0.1608288 0.9532164
## [163] {convolut,data} => {neural} 0.1302417 0.8918919
## [164] {convolut,data} => {network} 0.1410952 0.9662162
## [165] {model,convolut} => {neural} 0.1277750 0.8519737
## [166] {model,convolut} => {network} 0.1410952 0.9407895
## [167] {neural,convolut} => {network} 0.2683769 0.9801802
## [168] {network,convolut} => {neural} 0.2683769 0.8845528
## [169] {imag,paper} => {network} 0.1090281 0.7517007
## [170] {imag,method} => {network} 0.1386285 0.7356021
## [171] {imag,result} => {network} 0.1277750 0.7529070
## [172] {imag,perform} => {network} 0.1312284 0.7643678
## [173] {imag,propos} => {network} 0.1267884 0.7740964
## [174] {imag,train} => {network} 0.1524420 0.7763819
## [175] {imag,data} => {network} 0.1164282 0.7217125
## [176] {imag,model} => {network} 0.1149482 0.7236025
## [177] {imag,neural} => {network} 0.2057227 0.9652778
## [178] {imag,network} => {neural} 0.2057227 0.7973231
## [179] {show,paper} => {network} 0.1164282 0.7173252
## [180] {method,show} => {network} 0.1253083 0.7537092
## [181] {show,result} => {network} 0.1361618 0.7168831
## [182] {show,perform} => {network} 0.1233350 0.7267442
## [183] {propos,show} => {network} 0.1327084 0.7472222
## [184] {show,train} => {network} 0.1396152 0.7669377
## [185] {show,data} => {network} 0.1307351 0.7402235
## [186] {model,show} => {network} 0.1322151 0.7165775
## [187] {neural,show} => {network} 0.2116428 0.9683973
## [188] {network,show} => {neural} 0.2116428 0.8018692
## [189] {paper,approach} => {network} 0.1114948 0.7243590
## [190] {method,approach} => {network} 0.1248150 0.7249284
## [191] {result,approach} => {network} 0.1233350 0.7246377
## [192] {perform,approach} => {network} 0.1277750 0.7400000
## [193] {propos,approach} => {network} 0.1440553 0.7584416
## [194] {train,approach} => {network} 0.1366552 0.7847025
## [195] {data,approach} => {network} 0.1336951 0.7226667
## [196] {neural,approach} => {network} 0.2062161 0.9720930
## [197] {network,approach} => {neural} 0.2062161 0.8023033
## [198] {method,paper} => {network} 0.1277750 0.7076503
## [199] {paper,result} => {network} 0.1425752 0.7261307
## [200] {paper,perform} => {network} 0.1351751 0.7154047
## [201] {propos,paper} => {network} 0.1598421 0.7346939
## [202] {paper,train} => {network} 0.1312284 0.7665706
## [203] {neural,paper} => {network} 0.2215096 0.9655914
## [204] {network,paper} => {neural} 0.2215096 0.7701544
## [205] {method,perform} => {network} 0.1430686 0.7196030
## [206] {propos,method} => {network} 0.1726690 0.7322176
## [207] {method,train} => {network} 0.1830291 0.7961373
## [208] {method,data} => {network} 0.1583621 0.7345538
## [209] {method,neural} => {network} 0.2461766 0.9708171
## [210] {network,method} => {neural} 0.2461766 0.7920635
## [211] {result,perform} => {network} 0.1484953 0.7305825
## [212] {propos,result} => {network} 0.1593488 0.7291196
## [213] {result,train} => {network} 0.1692156 0.7903226
## [214] {data,result} => {network} 0.1470153 0.7215496
## [215] {neural,result} => {network} 0.2442033 0.9705882
## [216] {network,result} => {neural} 0.2442033 0.7832278
## [217] {propos,perform} => {network} 0.1529354 0.7345972
## [218] {perform,train} => {network} 0.1731623 0.7597403
## [219] {data,perform} => {network} 0.1568821 0.7243736
## [220] {neural,perform} => {network} 0.2520967 0.9714829
## [221] {network,perform} => {neural} 0.2520967 0.7996870
## [222] {propos,train} => {network} 0.1776024 0.7947020
## [223] {propos,data} => {network} 0.1628022 0.7568807
## [224] {model,propos} => {network} 0.1637889 0.7217391
## [225] {propos,neural} => {network} 0.2535767 0.9679849
## [226] {network,propos} => {neural} 0.2535767 0.7776097
## [227] {data,train} => {network} 0.1948693 0.7596154
## [228] {model,train} => {network} 0.1869758 0.7373541
## [229] {neural,train} => {network} 0.2955106 0.9771615
## [230] {network,train} => {neural} 0.2955106 0.8072776
## [231] {neural,data} => {network} 0.2580168 0.9721190
## [232] {network,data} => {neural} 0.2580168 0.7852853
## [233] {model,neural} => {network} 0.2747903 0.9686957
## [234] {model,network} => {neural} 0.2747903 0.8084180
## [235] {dataset,neural,train} => {network} 0.1011347 0.9715640
## [236] {dataset,network,train} => {neural} 0.1011347 0.8070866
## [237] {neural,convolut,base} => {network} 0.1011347 0.9855769
## [238] {network,convolut,base} => {neural} 0.1011347 0.8951965
## [239] {neural,base,train} => {network} 0.1036014 0.9813084
## [240] {network,base,train} => {neural} 0.1036014 0.8467742
## [241] {imag,neural,convolut} => {network} 0.1391219 0.9757785
## [242] {imag,network,convolut} => {neural} 0.1391219 0.8840125
## [243] {neural,convolut,approach} => {network} 0.1055747 0.9683258
## [244] {network,convolut,approach} => {neural} 0.1055747 0.9029536
## [245] {neural,convolut,paper} => {network} 0.1100148 0.9911111
## [246] {network,convolut,paper} => {neural} 0.1100148 0.8779528
## [247] {method,neural,convolut} => {network} 0.1307351 0.9742647
## [248] {network,method,convolut} => {neural} 0.1307351 0.8983051
## [249] {neural,convolut,result} => {network} 0.1277750 0.9810606
## [250] {network,convolut,result} => {neural} 0.1277750 0.8809524
## [251] {neural,convolut,perform} => {network} 0.1322151 0.9925926
## [252] {network,convolut,perform} => {neural} 0.1322151 0.8903654
## [253] {propos,neural,convolut} => {network} 0.1248150 0.9806202
## [254] {network,propos,convolut} => {neural} 0.1248150 0.8784722
## [255] {neural,convolut,train} => {network} 0.1445486 0.9799331
## [256] {network,convolut,train} => {neural} 0.1445486 0.8987730
## [257] {neural,convolut,data} => {network} 0.1277750 0.9810606
## [258] {network,convolut,data} => {neural} 0.1277750 0.9055944
## [259] {model,neural,convolut} => {network} 0.1258017 0.9845560
## [260] {model,network,convolut} => {neural} 0.1258017 0.8916084
## [261] {imag,method,neural} => {network} 0.1105081 0.9696970
## [262] {imag,network,method} => {neural} 0.1105081 0.7971530
## [263] {imag,neural,result} => {network} 0.1001480 0.9712919
## [264] {imag,network,result} => {neural} 0.1001480 0.7837838
## [265] {imag,neural,perform} => {network} 0.1075481 0.9688889
## [266] {imag,network,perform} => {neural} 0.1075481 0.8195489
## [267] {imag,neural,train} => {network} 0.1262950 0.9696970
## [268] {imag,network,train} => {neural} 0.1262950 0.8284790
## [269] {neural,show,result} => {network} 0.1065614 0.9557522
## [270] {network,show,result} => {neural} 0.1065614 0.7826087
## [271] {neural,show,perform} => {network} 0.1006413 0.9714286
## [272] {network,show,perform} => {neural} 0.1006413 0.8160000
## [273] {propos,neural,show} => {network} 0.1036014 0.9677419
## [274] {network,propos,show} => {neural} 0.1036014 0.7806691
## [275] {neural,show,train} => {network} 0.1129748 0.9786325
## [276] {network,show,train} => {neural} 0.1129748 0.8091873
## [277] {neural,show,data} => {network} 0.1031080 0.9675926
## [278] {network,show,data} => {neural} 0.1031080 0.7886792
## [279] {model,neural,show} => {network} 0.1036014 0.9633028
## [280] {model,network,show} => {neural} 0.1036014 0.7835821
## [281] {method,neural,approach} => {network} 0.1001480 0.9712919
## [282] {network,method,approach} => {neural} 0.1001480 0.8023715
## [283] {neural,perform,approach} => {network} 0.1026147 0.9857820
## [284] {network,perform,approach} => {neural} 0.1026147 0.8030888
## [285] {propos,neural,approach} => {network} 0.1144549 0.9789030
## [286] {network,propos,approach} => {neural} 0.1144549 0.7945205
## [287] {neural,train,approach} => {network} 0.1090281 0.9735683
## [288] {network,train,approach} => {neural} 0.1090281 0.7978339
## [289] {neural,data,approach} => {network} 0.1055747 0.9683258
## [290] {network,data,approach} => {neural} 0.1055747 0.7896679
## [291] {model,neural,approach} => {network} 0.1011347 0.9669811
## [292] {model,network,approach} => {neural} 0.1011347 0.8102767
## [293] {neural,paper,result} => {network} 0.1070548 0.9774775
## [294] {network,paper,result} => {neural} 0.1070548 0.7508651
## [295] {neural,paper,perform} => {network} 0.1050814 0.9726027
## [296] {network,paper,perform} => {neural} 0.1050814 0.7773723
## [297] {propos,neural,paper} => {network} 0.1184016 0.9716599
## [298] {network,propos,paper} => {neural} 0.1184016 0.7407407
## [299] {neural,paper,train} => {network} 0.1026147 0.9765258
## [300] {network,paper,train} => {neural} 0.1026147 0.7819549
## [301] {neural,data,paper} => {network} 0.1026147 0.9629630
## [302] {network,data,paper} => {neural} 0.1026147 0.7647059
## [303] {model,neural,paper} => {network} 0.1119882 0.9578059
## [304] {model,network,paper} => {neural} 0.1119882 0.8021201
## [305] {method,neural,result} => {network} 0.1090281 0.9692982
## [306] {network,method,result} => {neural} 0.1090281 0.7700348
## [307] {method,neural,perform} => {network} 0.1124815 0.9827586
## [308] {network,method,perform} => {neural} 0.1124815 0.7862069
## [309] {propos,method,train} => {network} 0.1021214 0.7992278
## [310] {propos,method,neural} => {network} 0.1332018 0.9747292
## [311] {network,propos,method} => {neural} 0.1332018 0.7714286
## [312] {method,data,train} => {network} 0.1045881 0.8000000
## [313] {method,neural,train} => {network} 0.1475086 0.9835526
## [314] {network,method,train} => {neural} 0.1475086 0.8059299
## [315] {method,neural,data} => {network} 0.1277750 0.9736842
## [316] {network,method,data} => {neural} 0.1277750 0.8068536
## [317] {model,method,neural} => {network} 0.1218550 0.9686275
## [318] {model,network,method} => {neural} 0.1218550 0.7967742
## [319] {neural,result,perform} => {network} 0.1179082 0.9676113
## [320] {network,result,perform} => {neural} 0.1179082 0.7940199
## [321] {propos,neural,result} => {network} 0.1193883 0.9641434
## [322] {network,propos,result} => {neural} 0.1193883 0.7492260
## [323] {neural,result,train} => {network} 0.1371485 0.9823322
## [324] {network,result,train} => {neural} 0.1371485 0.8104956
## [325] {neural,data,result} => {network} 0.1114948 0.9741379
## [326] {network,data,result} => {neural} 0.1114948 0.7583893
## [327] {model,neural,result} => {network} 0.1218550 0.9686275
## [328] {model,network,result} => {neural} 0.1218550 0.7891374
## [329] {propos,neural,perform} => {network} 0.1164282 0.9711934
## [330] {network,propos,perform} => {neural} 0.1164282 0.7612903
## [331] {neural,perform,train} => {network} 0.1406019 0.9827586
## [332] {network,perform,train} => {neural} 0.1406019 0.8119658
## [333] {neural,data,perform} => {network} 0.1287617 0.9812030
## [334] {network,data,perform} => {neural} 0.1287617 0.8207547
## [335] {model,neural,perform} => {network} 0.1253083 0.9731801
## [336] {model,network,perform} => {neural} 0.1253083 0.8246753
## [337] {propos,neural,train} => {network} 0.1415886 0.9761905
## [338] {network,propos,train} => {neural} 0.1415886 0.7972222
## [339] {propos,neural,data} => {network} 0.1277750 0.9736842
## [340] {network,propos,data} => {neural} 0.1277750 0.7848485
## [341] {model,propos,neural} => {network} 0.1297484 0.9740741
## [342] {model,network,propos} => {neural} 0.1297484 0.7921687
## [343] {model,data,train} => {network} 0.1055747 0.7157191
## [344] {neural,data,train} => {network} 0.1578688 0.9726444
## [345] {network,data,train} => {neural} 0.1578688 0.8101266
## [346] {model,neural,train} => {network} 0.1519487 0.9746835
## [347] {model,network,train} => {neural} 0.1519487 0.8126649
## [348] {model,neural,data} => {network} 0.1371485 0.9686411
## [349] {model,network,data} => {neural} 0.1371485 0.7853107
## lift count
## [1] 1.564742 204
## [2] 1.351618 215
## [3] 1.207966 219
## [4] 1.078433 209
## [5] 1.064075 204
## [6] 1.170634 235
## [7] 1.068437 213
## [8] 1.027615 212
## [9] 1.086873 228
## [10] 1.028938 233
## [11] 1.045033 241
## [12] 1.077618 256
## [13] 1.070897 273
## [14] 1.046650 269
## [15] 1.072895 275
## [16] 1.012240 270
## [17] 1.105431 301
## [18] 1.027778 277
## [19] 1.023179 285
## [20] 1.061546 292
## [21] 1.081397 323
## [22] 1.119360 353
## [23] 1.044945 323
## [24] 1.065912 328
## [25] 1.008037 341
## [26] 1.019618 352
## [27] 1.052038 361
## [28] 1.063460 376
## [29] 1.037745 377
## [30] 1.046003 380
## [31] 1.033514 397
## [32] 1.053325 409
## [33] 1.012433 410
## [34] 1.511406 555
## [35] 1.372671 615
## [36] 1.050107 523
## [37] 1.056519 535
## [38] 1.024655 521
## [39] 1.023665 630
## [40] 1.024602 632
## [41] 1.021003 639
## [42] 1.057329 661
## [43] 1.110401 742
## [44] 1.013524 666
## [45] 1.393473 1117
## [46] 1.393473 1117
## [47] 1.414374 224
## [48] 1.441230 224
## [49] 1.399825 211
## [50] 1.347714 211
## [51] 1.398886 206
## [52] 1.441589 206
## [53] 1.406612 213
## [54] 1.427994 213
## [55] 1.398302 203
## [56] 1.320627 203
## [57] 1.432903 213
## [58] 1.385683 213
## [59] 1.397641 233
## [60] 1.359682 233
## [61] 1.395816 223
## [62] 1.414077 223
## [63] 1.413691 218
## [64] 1.418210 218
## [65] 1.406612 213
## [66] 1.406520 213
## [67] 1.415435 234
## [68] 1.442178 234
## [69] 1.394842 218
## [70] 1.311359 218
## [71] 1.393615 212
## [72] 1.394673 212
## [73] 1.396334 258
## [74] 1.403024 258
## [75] 1.379895 231
## [76] 1.428702 231
## [77] 1.382045 216
## [78] 1.420988 216
## [79] 1.405217 245
## [80] 1.473775 245
## [81] 1.408198 224
## [82] 1.356744 224
## [83] 1.366340 261
## [84] 1.298715 261
## [85] 1.403030 230
## [86] 1.355687 230
## [87] 1.014173 205
## [88] 1.402852 267
## [89] 1.451967 267
## [90] 1.400111 248
## [91] 1.328085 248
## [92] 1.399482 244
## [93] 1.405199 244
## [94] 1.377488 266
## [95] 1.370172 266
## [96] 1.389469 277
## [97] 1.426834 277
## [98] 1.409948 285
## [99] 1.422165 285
## [100] 1.419065 276
## [101] 1.482550 276
## [102] 1.074430 203
## [103] 1.410350 289
## [104] 1.406172 289
## [105] 1.080968 217
## [106] 1.378586 271
## [107] 1.344665 271
## [108] 1.402835 305
## [109] 1.424820 305
## [110] 1.393041 299
## [111] 1.411810 299
## [112] 1.044732 209
## [113] 1.393041 299
## [114] 1.393086 299
## [115] 1.400053 283
## [116] 1.424325 283
## [117] 1.150614 211
## [118] 1.388711 300
## [119] 1.386710 300
## [120] 1.139496 224
## [121] 1.382746 316
## [122] 1.398120 316
## [123] 1.162779 210
## [124] 1.163392 219
## [125] 1.079723 213
## [126] 1.404194 317
## [127] 1.361394 317
## [128] 1.369869 216
## [129] 1.053388 210
## [130] 1.056045 223
## [131] 1.098097 254
## [132] 1.021990 208
## [133] 1.367008 320
## [134] 1.370926 320
## [135] 1.541569 208
## [136] 1.391036 229
## [137] 1.083707 204
## [138] 1.045725 223
## [139] 1.020741 212
## [140] 1.044177 235
## [141] 1.115714 248
## [142] 1.048738 220
## [143] 1.401944 372
## [144] 1.423568 372
## [145] 1.529001 289
## [146] 1.383260 319
## [147] 1.495202 206
## [148] 1.386091 233
## [149] 1.565267 221
## [150] 1.375776 237
## [151] 1.508444 225
## [152] 1.395672 254
## [153] 1.566452 272
## [154] 1.392430 295
## [155] 1.530415 264
## [156] 1.396869 294
## [157] 1.510365 270
## [158] 1.380028 301
## [159] 1.525848 258
## [160] 1.396006 288
## [161] 1.535653 299
## [162] 1.372280 326
## [163] 1.566607 264
## [164] 1.390995 286
## [165] 1.496491 259
## [166] 1.354389 286
## [167] 1.411097 544
## [168] 1.553716 544
## [169] 1.082171 221
## [170] 1.058995 281
## [171] 1.083908 259
## [172] 1.100407 266
## [173] 1.114413 257
## [174] 1.117703 309
## [175] 1.039000 236
## [176] 1.041720 233
## [177] 1.389644 417
## [178] 1.400497 417
## [179] 1.032683 236
## [180] 1.085063 254
## [181] 1.032047 276
## [182] 1.046243 250
## [183] 1.075724 269
## [184] 1.104107 283
## [185] 1.065648 265
## [186] 1.031607 268
## [187] 1.394134 429
## [188] 1.408482 429
## [189] 1.042809 226
## [190] 1.043629 253
## [191] 1.043211 250
## [192] 1.065327 259
## [193] 1.091876 292
## [194] 1.129682 277
## [195] 1.040373 271
## [196] 1.399455 418
## [197] 1.409245 418
## [198] 1.018755 259
## [199] 1.045360 289
## [200] 1.029919 274
## [201] 1.057688 324
## [202] 1.103579 266
## [203] 1.390095 449
## [204] 1.352775 449
## [205] 1.035963 290
## [206] 1.054123 350
## [207] 1.146144 371
## [208] 1.057486 321
## [209] 1.397618 499
## [210] 1.391259 499
## [211] 1.051769 301
## [212] 1.049663 323
## [213] 1.137773 343
## [214] 1.038765 298
## [215] 1.397289 495
## [216] 1.375739 495
## [217] 1.057549 310
## [218] 1.093745 351
## [219] 1.042830 318
## [220] 1.398577 511
## [221] 1.404650 511
## [222] 1.144077 360
## [223] 1.089629 330
## [224] 1.039038 332
## [225] 1.393541 514
## [226] 1.365871 514
## [227] 1.093566 395
## [228] 1.061518 379
## [229] 1.406752 599
## [230] 1.417982 599
## [231] 1.399492 523
## [232] 1.379353 523
## [233] 1.394564 557
## [234] 1.419986 557
## [235] 1.398693 205
## [236] 1.417647 205
## [237] 1.418867 205
## [238] 1.572412 205
## [239] 1.412722 210
## [240] 1.487358 210
## [241] 1.404761 282
## [242] 1.552767 282
## [243] 1.394032 214
## [244] 1.586037 214
## [245] 1.426834 223
## [246] 1.542123 223
## [247] 1.402581 265
## [248] 1.577872 265
## [249] 1.412365 259
## [250] 1.547392 259
## [251] 1.428967 268
## [252] 1.563926 268
## [253] 1.411731 253
## [254] 1.543036 253
## [255] 1.410742 293
## [256] 1.578694 293
## [257] 1.412365 259
## [258] 1.590676 259
## [259] 1.417397 255
## [260] 1.566109 255
## [261] 1.396006 224
## [262] 1.400199 224
## [263] 1.398302 203
## [264] 1.376716 203
## [265] 1.394842 218
## [266] 1.439537 218
## [267] 1.396006 256
## [268] 1.455223 256
## [269] 1.375930 216
## [270] 1.374651 216
## [271] 1.398498 204
## [272] 1.433303 204
## [273] 1.393191 210
## [274] 1.371245 210
## [275] 1.408869 229
## [276] 1.421337 229
## [277] 1.392976 209
## [278] 1.385314 209
## [279] 1.386800 210
## [280] 1.376361 210
## [281] 1.398302 203
## [282] 1.409365 203
## [283] 1.419162 208
## [284] 1.410625 208
## [285] 1.409259 232
## [286] 1.395575 232
## [287] 1.401579 221
## [288] 1.401395 221
## [289] 1.394032 214
## [290] 1.387051 214
## [291] 1.392096 205
## [292] 1.423250 205
## [293] 1.407207 217
## [294] 1.318894 217
## [295] 1.400189 213
## [296] 1.365454 213
## [297] 1.398831 240
## [298] 1.301110 240
## [299] 1.405837 208
## [300] 1.373503 208
## [301] 1.386311 208
## [302] 1.343205 208
## [303] 1.378887 227
## [304] 1.408923 227
## [305] 1.395431 221
## [306] 1.352566 221
## [307] 1.414809 228
## [308] 1.380972 228
## [309] 1.150593 207
## [310] 1.403250 270
## [311] 1.355014 270
## [312] 1.151705 212
## [313] 1.415953 299
## [314] 1.415615 299
## [315] 1.401746 259
## [316] 1.417238 259
## [317] 1.394466 247
## [318] 1.399533 247
## [319] 1.393003 239
## [320] 1.394695 239
## [321] 1.388010 242
## [322] 1.316015 242
## [323] 1.414196 278
## [324] 1.423635 278
## [325] 1.402399 226
## [326] 1.332110 226
## [327] 1.394466 247
## [328] 1.386119 247
## [329] 1.398160 236
## [330] 1.337206 236
## [331] 1.414809 285
## [332] 1.426217 285
## [333] 1.412570 261
## [334] 1.441655 261
## [335] 1.401020 254
## [336] 1.448541 254
## [337] 1.405354 287
## [338] 1.400320 287
## [339] 1.401746 259
## [340] 1.378586 259
## [341] 1.402307 263
## [342] 1.391444 263
## [343] 1.030371 214
## [344] 1.400249 320
## [345] 1.422987 320
## [346] 1.403184 308
## [347] 1.427445 308
## [348] 1.394485 278
## [349] 1.379398 278
insp_list
## [[1]]
## NULL
##
## [[2]]
## NULL
##
## [[3]]
## NULL
##
## [[4]]
## lhs rhs support confidence lift count
## [1] {experiment} => {result} 0.1078431 0.8461538 1.798077 11
## [2] {practic} => {network} 0.1078431 0.9166667 1.246667 11
## [3] {robust} => {network} 0.1176471 1.0000000 1.360000 12
## [4] {optim} => {network} 0.1078431 0.9166667 1.246667 11
## [5] {find} => {perform} 0.1176471 0.9230769 1.810651 12
## [6] {util} => {network} 0.1078431 0.9166667 1.246667 11
## [7] {order} => {network} 0.1176471 0.9230769 1.255385 12
## [8] {cnn} => {convolut} 0.1078431 0.8461538 2.213018 11
## [9] {cnn} => {neural} 0.1176471 0.9230769 1.518610 12
## [10] {cnn} => {network} 0.1176471 0.9230769 1.255385 12
##
## [[5]]
## lhs rhs support confidence lift count
## [1] {layer} => {network} 0.1181435 0.8484848 1.175970 28
## [2] {estim} => {network} 0.1054852 0.8333333 1.154971 25
## [3] {implement} => {network} 0.1054852 0.8064516 1.117714 25
## [4] {success} => {network} 0.1054852 0.7575758 1.049973 25
## [5] {experiment} => {result} 0.1139241 0.8437500 1.851562 27
## [6] {experiment} => {network} 0.1012658 0.7500000 1.039474 24
## [7] {increas} => {network} 0.1012658 0.7741935 1.073005 24
## [8] {input} => {network} 0.1223629 0.8285714 1.148371 29
## [9] {research} => {data} 0.1054852 0.7352941 1.613562 25
## [10] {order} => {network} 0.1265823 0.8333333 1.154971 30
##
## [[6]]
## lhs rhs support confidence lift count
## [1] {cnn} => {convolut} 0.1012862 0.8513514 2.674447 63
## [2] {cnn} => {neural} 0.1045016 0.8783784 1.556557 65
## [3] {cnn} => {network} 0.1125402 0.9459459 1.337224 70
## [4] {outperform} => {network} 0.1012862 0.7590361 1.073001 63
## [5] {general} => {network} 0.1045016 0.7738095 1.093885 65
## [6] {number} => {network} 0.1028939 0.7901235 1.116947 64
## [7] {input} => {network} 0.1093248 0.8500000 1.201591 68
## [8] {layer} => {network} 0.1189711 0.8809524 1.245346 74
## [9] {effici} => {network} 0.1061093 0.7415730 1.048315 66
## [10] {test} => {network} 0.1157556 0.7578947 1.071388 72
##
## [[7]]
## lhs rhs support confidence lift count
## [1] {structur} => {network} 0.1058824 0.7883212 1.161976 108
## [2] {optim} => {network} 0.1088235 0.7449664 1.098072 111
## [3] {complex} => {network} 0.1009804 0.7202797 1.061684 103
## [4] {input} => {network} 0.1137255 0.7837838 1.155288 116
## [5] {learn} => {network} 0.1215686 0.7898089 1.164169 124
## [6] {stateoftheart} => {network} 0.1205882 0.7068966 1.041957 123
## [7] {classif} => {network} 0.1333333 0.7311828 1.077755 136
## [8] {effici} => {network} 0.1294118 0.7252747 1.069047 132
## [9] {design} => {network} 0.1313725 0.7322404 1.079314 134
## [10] {appli} => {network} 0.1392157 0.7434555 1.095845 142
##
## [[8]]
## lhs rhs support confidence lift count
## [1] {cnn} => {neural} 0.1006413 0.8908297 1.564742 204
## [2] {cnn} => {network} 0.1060681 0.9388646 1.351618 215
## [3] {layer} => {network} 0.1080414 0.8390805 1.207966 219
## [4] {general} => {network} 0.1031080 0.7491039 1.078433 209
## [5] {function} => {network} 0.1006413 0.7391304 1.064075 204
## [6] {input} => {network} 0.1159349 0.8131488 1.170634 235
## [7] {complex} => {network} 0.1050814 0.7421603 1.068437 213
## [8] {number} => {network} 0.1045881 0.7138047 1.027615 212
## [9] {optim} => {network} 0.1124815 0.7549669 1.086873 228
## [10] {test} => {network} 0.1149482 0.7147239 1.028938 233
15년도 분석을 보면, experiment -> result가 1위다. 즉, 실험을 통해 결과를 도출하는 방식이 많이 이루어졌다. 그 후 16년도부터 layer라는 단어가 급증했고, 17년도 부터는 CNN 기법이 대세를 이루었다.